Stats
Contents
Stats¶
SymPy statistics module
Introduces a random variable type into the SymPy language.
Random variables may be declared using prebuilt functions such as Normal, Exponential, Coin, Die, etc… or built with functions like FiniteRV.
Queries on random expressions can be made using the functions
Expression |
Meaning |
|
Probability |
|
Expected value |
|
Entropy |
|
Variance |
|
Probability Density Function |
|
Produce a realization |
|
Where the condition is true |
Examples¶
>>> from sympy.stats import P, E, variance, Die, Normal
>>> from sympy import simplify
>>> X, Y = Die('X', 6), Die('Y', 6) # Define two six sided dice
>>> Z = Normal('Z', 0, 1) # Declare a Normal random variable with mean 0, std 1
>>> P(X>3) # Probability X is greater than 3
1/2
>>> E(X+Y) # Expectation of the sum of two dice
7
>>> variance(X+Y) # Variance of the sum of two dice
35/6
>>> simplify(P(Z>1)) # Probability of Z being greater than 1
1/2 - erf(sqrt(2)/2)/2
One could also create custom distribution and define custom random variables as follows:
If you want to create a Continuous Random Variable:
>>> from sympy.stats import ContinuousRV, P, E
>>> from sympy import exp, Symbol, Interval, oo
>>> x = Symbol('x')
>>> pdf = exp(-x) # pdf of the Continuous Distribution
>>> Z = ContinuousRV(x, pdf, set=Interval(0, oo))
>>> E(Z)
1
>>> P(Z > 5)
exp(-5)
1.1 To create an instance of Continuous Distribution:
>>> from sympy.stats import ContinuousDistributionHandmade
>>> from sympy import Lambda
>>> dist = ContinuousDistributionHandmade(Lambda(x, pdf), set=Interval(0, oo))
>>> dist.pdf(x)
exp(-x)
If you want to create a Discrete Random Variable:
>>> from sympy.stats import DiscreteRV, P, E
>>> from sympy import Symbol, S
>>> p = S(1)/2
>>> x = Symbol('x', integer=True, positive=True)
>>> pdf = p*(1 - p)**(x - 1)
>>> D = DiscreteRV(x, pdf, set=S.Naturals)
>>> E(D)
2
>>> P(D > 3)
1/8
2.1 To create an instance of Discrete Distribution:
>>> from sympy.stats import DiscreteDistributionHandmade
>>> from sympy import Lambda
>>> dist = DiscreteDistributionHandmade(Lambda(x, pdf), set=S.Naturals)
>>> dist.pdf(x)
2**(1 - x)/2
If you want to create a Finite Random Variable:
>>> from sympy.stats import FiniteRV, P, E
>>> from sympy import Rational, Eq
>>> pmf = {1: Rational(1, 3), 2: Rational(1, 6), 3: Rational(1, 4), 4: Rational(1, 4)}
>>> X = FiniteRV('X', pmf)
>>> E(X)
29/12
>>> P(X > 3)
1/4
3.1 To create an instance of Finite Distribution:
>>> from sympy.stats import FiniteDistributionHandmade
>>> dist = FiniteDistributionHandmade(pmf)
>>> dist.pmf(x)
Lambda(x, Piecewise((1/3, Eq(x, 1)), (1/6, Eq(x, 2)), (1/4, Eq(x, 3) | Eq(x, 4)), (0, True)))
Random Variable Types¶
Finite Types¶
- sympy.stats.DiscreteUniform(name, items)[source]¶
Create a Finite Random Variable representing a uniform distribution over the input set.
- Parameters
items: list/tuple
Items over which Uniform distribution is to be made
- Returns
RandomSymbol
Examples
>>> from sympy.stats import DiscreteUniform, density >>> from sympy import symbols
>>> X = DiscreteUniform('X', symbols('a b c')) # equally likely over a, b, c >>> density(X).dict {a: 1/3, b: 1/3, c: 1/3}
>>> Y = DiscreteUniform('Y', list(range(5))) # distribution over a range >>> density(Y).dict {0: 1/5, 1: 1/5, 2: 1/5, 3: 1/5, 4: 1/5}
References
- sympy.stats.Die(name, sides=6)[source]¶
Create a Finite Random Variable representing a fair die.
- Parameters
sides: Integer
Represents the number of sides of the Die, by default is 6
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Die, density >>> from sympy import Symbol
>>> D6 = Die('D6', 6) # Six sided Die >>> density(D6).dict {1: 1/6, 2: 1/6, 3: 1/6, 4: 1/6, 5: 1/6, 6: 1/6}
>>> D4 = Die('D4', 4) # Four sided Die >>> density(D4).dict {1: 1/4, 2: 1/4, 3: 1/4, 4: 1/4}
>>> n = Symbol('n', positive=True, integer=True) >>> Dn = Die('Dn', n) # n sided Die >>> density(Dn).dict Density(DieDistribution(n)) >>> density(Dn).dict.subs(n, 4).doit() {1: 1/4, 2: 1/4, 3: 1/4, 4: 1/4}
- sympy.stats.Bernoulli(name, p, succ=1, fail=0)[source]¶
Create a Finite Random Variable representing a Bernoulli process.
- Parameters
p : Rational number between 0 and 1
Represents probability of success
succ : Integer/symbol/string
Represents event of success
fail : Integer/symbol/string
Represents event of failure
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Bernoulli, density >>> from sympy import S
>>> X = Bernoulli('X', S(3)/4) # 1-0 Bernoulli variable, probability = 3/4 >>> density(X).dict {0: 1/4, 1: 3/4}
>>> X = Bernoulli('X', S.Half, 'Heads', 'Tails') # A fair coin toss >>> density(X).dict {Heads: 1/2, Tails: 1/2}
References
- sympy.stats.Coin(name, p=1 / 2)[source]¶
Create a Finite Random Variable representing a Coin toss.
- Parameters
p : Rational Numeber between 0 and 1
Represents probability of getting “Heads”, by default is Half
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Coin, density >>> from sympy import Rational
>>> C = Coin('C') # A fair coin toss >>> density(C).dict {H: 1/2, T: 1/2}
>>> C2 = Coin('C2', Rational(3, 5)) # An unfair coin >>> density(C2).dict {H: 3/5, T: 2/5}
See also
References
- sympy.stats.Binomial(name, n, p, succ=1, fail=0)[source]¶
Create a Finite Random Variable representing a binomial distribution.
- Parameters
n : Positive Integer
Represents number of trials
p : Rational Number between 0 and 1
Represents probability of success
succ : Integer/symbol/string
Represents event of success, by default is 1
fail : Integer/symbol/string
Represents event of failure, by default is 0
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Binomial, density >>> from sympy import S, Symbol
>>> X = Binomial('X', 4, S.Half) # Four "coin flips" >>> density(X).dict {0: 1/16, 1: 1/4, 2: 3/8, 3: 1/4, 4: 1/16}
>>> n = Symbol('n', positive=True, integer=True) >>> p = Symbol('p', positive=True) >>> X = Binomial('X', n, S.Half) # n "coin flips" >>> density(X).dict Density(BinomialDistribution(n, 1/2, 1, 0)) >>> density(X).dict.subs(n, 4).doit() {0: 1/16, 1: 1/4, 2: 3/8, 3: 1/4, 4: 1/16}
References
- sympy.stats.BetaBinomial(name, n, alpha, beta)[source]¶
Create a Finite Random Variable representing a Beta-binomial distribution.
- Parameters
n : Positive Integer
Represents number of trials
alpha : Real positive number
beta : Real positive number
- Returns
RandomSymbol
Examples
>>> from sympy.stats import BetaBinomial, density
>>> X = BetaBinomial('X', 2, 1, 1) >>> density(X).dict {0: 1/3, 1: 2*beta(2, 2), 2: 1/3}
References
- sympy.stats.Hypergeometric(name, N, m, n)[source]¶
Create a Finite Random Variable representing a hypergeometric distribution.
- Parameters
N : Positive Integer
Represents finite population of size N.
m : Positive Integer
Represents number of trials with required feature.
n : Positive Integer
Represents numbers of draws.
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Hypergeometric, density
>>> X = Hypergeometric('X', 10, 5, 3) # 10 marbles, 5 white (success), 3 draws >>> density(X).dict {0: 1/12, 1: 5/12, 2: 5/12, 3: 1/12}
References
- sympy.stats.FiniteRV(name, density, **kwargs)[source]¶
Create a Finite Random Variable given a dict representing the density.
- Parameters
name : Symbol
Represents name of the random variable.
density: dict
Dictionary containing the pdf of finite distribution
check : bool
If True, it will check whether the given density integrates to 1 over the given set. If False, it will not perform this check. Default is False.
- Returns
RandomSymbol
Examples
>>> from sympy.stats import FiniteRV, P, E
>>> density = {0: .1, 1: .2, 2: .3, 3: .4} >>> X = FiniteRV('X', density)
>>> E(X) 2.00000000000000 >>> P(X >= 2) 0.700000000000000
Discrete Types¶
- sympy.stats.Geometric(name, p)[source]¶
Create a discrete random variable with a Geometric distribution.
- Parameters
p : A probability between 0 and 1
- Returns
RandomSymbol
Explanation
The density of the Geometric distribution is given by
\[f(k) := p (1 - p)^{k - 1}\]Examples
>>> from sympy.stats import Geometric, density, E, variance >>> from sympy import Symbol, S
>>> p = S.One / 5 >>> z = Symbol("z")
>>> X = Geometric("x", p)
>>> density(X)(z) (4/5)**(z - 1)/5
>>> E(X) 5
>>> variance(X) 20
References
- sympy.stats.Hermite(name, a1, a2)[source]¶
Create a discrete random variable with a Hermite distribution.
- Parameters
a1: A Positive number greater than equal to 0.
a2: A Positive number greater than equal to 0.
- Returns
RandomSymbol
Explanation
The density of the Hermite distribution is given by
\[f(x):= e^{-a_1 -a_2}\sum_{j=0}^{\left \lfloor x/2 \right \rfloor} \frac{a_{1}^{x-2j}a_{2}^{j}}{(x-2j)!j!}\]Examples
>>> from sympy.stats import Hermite, density, E, variance >>> from sympy import Symbol
>>> a1 = Symbol("a1", positive=True) >>> a2 = Symbol("a2", positive=True) >>> x = Symbol("x")
>>> H = Hermite("H", a1=5, a2=4)
>>> density(H)(2) 33*exp(-9)/2
>>> E(H) 13
>>> variance(H) 21
References
- sympy.stats.Poisson(name, lamda)[source]¶
Create a discrete random variable with a Poisson distribution.
- Parameters
lamda: Positive number, a rate
- Returns
RandomSymbol
Explanation
The density of the Poisson distribution is given by
\[f(k) := \frac{\lambda^{k} e^{- \lambda}}{k!}\]Examples
>>> from sympy.stats import Poisson, density, E, variance >>> from sympy import Symbol, simplify
>>> rate = Symbol("lambda", positive=True) >>> z = Symbol("z")
>>> X = Poisson("x", rate)
>>> density(X)(z) lambda**z*exp(-lambda)/factorial(z)
>>> E(X) lambda
>>> simplify(variance(X)) lambda
References
- sympy.stats.Logarithmic(name, p)[source]¶
Create a discrete random variable with a Logarithmic distribution.
- Parameters
p: A value between 0 and 1
- Returns
RandomSymbol
Explanation
The density of the Logarithmic distribution is given by
\[f(k) := \frac{-p^k}{k \ln{(1 - p)}}\]Examples
>>> from sympy.stats import Logarithmic, density, E, variance >>> from sympy import Symbol, S
>>> p = S.One / 5 >>> z = Symbol("z")
>>> X = Logarithmic("x", p)
>>> density(X)(z) -1/(5**z*z*log(4/5))
>>> E(X) -1/(-4*log(5) + 8*log(2))
>>> variance(X) -1/((-4*log(5) + 8*log(2))*(-2*log(5) + 4*log(2))) + 1/(-64*log(2)*log(5) + 64*log(2)**2 + 16*log(5)**2) - 10/(-32*log(5) + 64*log(2))
References
- sympy.stats.NegativeBinomial(name, r, p)[source]¶
Create a discrete random variable with a Negative Binomial distribution.
- Parameters
r: A positive value
p: A value between 0 and 1
- Returns
RandomSymbol
Explanation
The density of the Negative Binomial distribution is given by
\[f(k) := \binom{k + r - 1}{k} (1 - p)^r p^k\]Examples
>>> from sympy.stats import NegativeBinomial, density, E, variance >>> from sympy import Symbol, S
>>> r = 5 >>> p = S.One / 5 >>> z = Symbol("z")
>>> X = NegativeBinomial("x", r, p)
>>> density(X)(z) 1024*binomial(z + 4, z)/(3125*5**z)
>>> E(X) 5/4
>>> variance(X) 25/16
References
- sympy.stats.Skellam(name, mu1, mu2)[source]¶
Create a discrete random variable with a Skellam distribution.
- Parameters
mu1: A non-negative value
mu2: A non-negative value
- Returns
RandomSymbol
Explanation
The Skellam is the distribution of the difference N1 - N2 of two statistically independent random variables N1 and N2 each Poisson-distributed with respective expected values mu1 and mu2.
The density of the Skellam distribution is given by
\[f(k) := e^{-(\mu_1+\mu_2)}(\frac{\mu_1}{\mu_2})^{k/2}I_k(2\sqrt{\mu_1\mu_2})\]Examples
>>> from sympy.stats import Skellam, density, E, variance >>> from sympy import Symbol, pprint
>>> z = Symbol("z", integer=True) >>> mu1 = Symbol("mu1", positive=True) >>> mu2 = Symbol("mu2", positive=True) >>> X = Skellam("x", mu1, mu2)
>>> pprint(density(X)(z), use_unicode=False) z - 2 /mu1\ -mu1 - mu2 / _____ _____\ |---| *e *besseli\z, 2*\/ mu1 *\/ mu2 / \mu2/ >>> E(X) mu1 - mu2 >>> variance(X).expand() mu1 + mu2
References
- sympy.stats.YuleSimon(name, rho)[source]¶
Create a discrete random variable with a Yule-Simon distribution.
- Parameters
rho: A positive value
- Returns
RandomSymbol
Explanation
The density of the Yule-Simon distribution is given by
\[f(k) := \rho B(k, \rho + 1)\]Examples
>>> from sympy.stats import YuleSimon, density, E, variance >>> from sympy import Symbol, simplify
>>> p = 5 >>> z = Symbol("z")
>>> X = YuleSimon("x", p)
>>> density(X)(z) 5*beta(z, 6)
>>> simplify(E(X)) 5/4
>>> simplify(variance(X)) 25/48
References
- sympy.stats.Zeta(name, s)[source]¶
Create a discrete random variable with a Zeta distribution.
- Parameters
s: A value greater than 1
- Returns
RandomSymbol
Explanation
The density of the Zeta distribution is given by
\[f(k) := \frac{1}{k^s \zeta{(s)}}\]Examples
>>> from sympy.stats import Zeta, density, E, variance >>> from sympy import Symbol
>>> s = 5 >>> z = Symbol("z")
>>> X = Zeta("x", s)
>>> density(X)(z) 1/(z**5*zeta(5))
>>> E(X) pi**4/(90*zeta(5))
>>> variance(X) -pi**8/(8100*zeta(5)**2) + zeta(3)/zeta(5)
References
Continuous Types¶
- sympy.stats.Arcsin(name, a=0, b=1)[source]¶
Create a Continuous Random Variable with an arcsin distribution.
The density of the arcsin distribution is given by
\[f(x) := \frac{1}{\pi\sqrt{(x-a)(b-x)}}\]with \(x \in (a,b)\). It must hold that \(-\infty < a < b < \infty\).
- Parameters
a : Real number, the left interval boundary
b : Real number, the right interval boundary
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Arcsin, density, cdf >>> from sympy import Symbol
>>> a = Symbol("a", real=True) >>> b = Symbol("b", real=True) >>> z = Symbol("z")
>>> X = Arcsin("x", a, b)
>>> density(X)(z) 1/(pi*sqrt((-a + z)*(b - z)))
>>> cdf(X)(z) Piecewise((0, a > z), (2*asin(sqrt((-a + z)/(-a + b)))/pi, b >= z), (1, True))
References
- sympy.stats.Benini(name, alpha, beta, sigma)[source]¶
Create a Continuous Random Variable with a Benini distribution.
The density of the Benini distribution is given by
\[f(x) := e^{-\alpha\log{\frac{x}{\sigma}} -\beta\log^2\left[{\frac{x}{\sigma}}\right]} \left(\frac{\alpha}{x}+\frac{2\beta\log{\frac{x}{\sigma}}}{x}\right)\]This is a heavy-tailed distribution and is also known as the log-Rayleigh distribution.
- Parameters
alpha : Real number, \(\alpha > 0\), a shape
beta : Real number, \(\beta > 0\), a shape
sigma : Real number, \(\sigma > 0\), a scale
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Benini, density, cdf >>> from sympy import Symbol, pprint
>>> alpha = Symbol("alpha", positive=True) >>> beta = Symbol("beta", positive=True) >>> sigma = Symbol("sigma", positive=True) >>> z = Symbol("z")
>>> X = Benini("x", alpha, beta, sigma)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) / / z \\ / z \ 2/ z \ | 2*beta*log|-----|| - alpha*log|-----| - beta*log |-----| |alpha \sigma/| \sigma/ \sigma/ |----- + -----------------|*e \ z z /
>>> cdf(X)(z) Piecewise((1 - exp(-alpha*log(z/sigma) - beta*log(z/sigma)**2), sigma <= z), (0, True))
References
- sympy.stats.Beta(name, alpha, beta)[source]¶
Create a Continuous Random Variable with a Beta distribution.
The density of the Beta distribution is given by
\[f(x) := \frac{x^{\alpha-1}(1-x)^{\beta-1}} {\mathrm{B}(\alpha,\beta)}\]with \(x \in [0,1]\).
- Parameters
alpha : Real number, \(\alpha > 0\), a shape
beta : Real number, \(\beta > 0\), a shape
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Beta, density, E, variance >>> from sympy import Symbol, simplify, pprint, factor
>>> alpha = Symbol("alpha", positive=True) >>> beta = Symbol("beta", positive=True) >>> z = Symbol("z")
>>> X = Beta("x", alpha, beta)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) alpha - 1 beta - 1 z *(1 - z) -------------------------- B(alpha, beta)
>>> simplify(E(X)) alpha/(alpha + beta)
>>> factor(simplify(variance(X))) alpha*beta/((alpha + beta)**2*(alpha + beta + 1))
References
- sympy.stats.BetaNoncentral(name, alpha, beta, lamda)[source]¶
Create a Continuous Random Variable with a Type I Noncentral Beta distribution.
The density of the Noncentral Beta distribution is given by
\[f(x) := \sum_{k=0}^\infty e^{-\lambda/2}\frac{(\lambda/2)^k}{k!} \frac{x^{\alpha+k-1}(1-x)^{\beta-1}}{\mathrm{B}(\alpha+k,\beta)}\]with \(x \in [0,1]\).
- Parameters
alpha : Real number, \(\alpha > 0\), a shape
beta : Real number, \(\beta > 0\), a shape
lamda: Real number, `lambda ge 0`, noncentrality parameter
- Returns
RandomSymbol
Examples
>>> from sympy.stats import BetaNoncentral, density, cdf >>> from sympy import Symbol, pprint
>>> alpha = Symbol("alpha", positive=True) >>> beta = Symbol("beta", positive=True) >>> lamda = Symbol("lamda", nonnegative=True) >>> z = Symbol("z")
>>> X = BetaNoncentral("x", alpha, beta, lamda)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) oo _____ \ ` \ -lamda \ k ------- \ k + alpha - 1 /lamda\ beta - 1 2 ) z *|-----| *(1 - z) *e / \ 2 / / ------------------------------------------------ / B(k + alpha, beta)*k! /____, k = 0
Compute cdf with specific ‘x’, ‘alpha’, ‘beta’ and ‘lamda’ values as follows : >>> cdf(BetaNoncentral(“x”, 1, 1, 1), evaluate=False)(2).doit() 2*exp(1/2)
The argument evaluate=False prevents an attempt at evaluation of the sum for general x, before the argument 2 is passed.
References
- sympy.stats.BetaPrime(name, alpha, beta)[source]¶
Create a continuous random variable with a Beta prime distribution.
The density of the Beta prime distribution is given by
\[f(x) := \frac{x^{\alpha-1} (1+x)^{-\alpha -\beta}}{B(\alpha,\beta)}\]with \(x > 0\).
- Parameters
alpha : Real number, \(\alpha > 0\), a shape
beta : Real number, \(\beta > 0\), a shape
- Returns
RandomSymbol
Examples
>>> from sympy.stats import BetaPrime, density >>> from sympy import Symbol, pprint
>>> alpha = Symbol("alpha", positive=True) >>> beta = Symbol("beta", positive=True) >>> z = Symbol("z")
>>> X = BetaPrime("x", alpha, beta)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) alpha - 1 -alpha - beta z *(z + 1) ------------------------------- B(alpha, beta)
References
- sympy.stats.BoundedPareto(name, alpha, left, right)[source]¶
Create a continuous random variable with a Bounded Pareto distribution.
The density of the Bounded Pareto distribution is given by
\[f(x) := \frac{\alpha L^{\alpha}x^{-\alpha-1}}{1-(\frac{L}{H})^{\alpha}}\]- Parameters
alpha : Real Number, \(alpha > 0\)
Shape parameter
left : Real Number, \(left > 0\)
Location parameter
right : Real Number, \(right > left\)
Location parameter
- Returns
RandomSymbol
Examples
>>> from sympy.stats import BoundedPareto, density, cdf, E >>> from sympy import symbols >>> L, H = symbols('L, H', positive=True) >>> X = BoundedPareto('X', 2, L, H) >>> x = symbols('x') >>> density(X)(x) 2*L**2/(x**3*(1 - L**2/H**2)) >>> cdf(X)(x) Piecewise((-H**2*L**2/(x**2*(H**2 - L**2)) + H**2/(H**2 - L**2), L <= x), (0, True)) >>> E(X).simplify() 2*H*L/(H + L)
References
- sympy.stats.Cauchy(name, x0, gamma)[source]¶
Create a continuous random variable with a Cauchy distribution.
The density of the Cauchy distribution is given by
\[f(x) := \frac{1}{\pi \gamma [1 + {(\frac{x-x_0}{\gamma})}^2]}\]- Parameters
x0 : Real number, the location
gamma : Real number, \(\gamma > 0\), a scale
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Cauchy, density >>> from sympy import Symbol
>>> x0 = Symbol("x0") >>> gamma = Symbol("gamma", positive=True) >>> z = Symbol("z")
>>> X = Cauchy("x", x0, gamma)
>>> density(X)(z) 1/(pi*gamma*(1 + (-x0 + z)**2/gamma**2))
References
- sympy.stats.Chi(name, k)[source]¶
Create a continuous random variable with a Chi distribution.
The density of the Chi distribution is given by
\[f(x) := \frac{2^{1-k/2}x^{k-1}e^{-x^2/2}}{\Gamma(k/2)}\]with \(x \geq 0\).
- Parameters
k : Positive integer, The number of degrees of freedom
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Chi, density, E >>> from sympy import Symbol, simplify
>>> k = Symbol("k", integer=True) >>> z = Symbol("z")
>>> X = Chi("x", k)
>>> density(X)(z) 2**(1 - k/2)*z**(k - 1)*exp(-z**2/2)/gamma(k/2)
>>> simplify(E(X)) sqrt(2)*gamma(k/2 + 1/2)/gamma(k/2)
References
- sympy.stats.ChiNoncentral(name, k, l)[source]¶
Create a continuous random variable with a non-central Chi distribution.
- Parameters
k : A positive Integer, \(k > 0\)
The number of degrees of freedom.
lambda : Real number, \(\lambda > 0\)
Shift parameter.
- Returns
RandomSymbol
Explanation
The density of the non-central Chi distribution is given by
\[f(x) := \frac{e^{-(x^2+\lambda^2)/2} x^k\lambda} {(\lambda x)^{k/2}} I_{k/2-1}(\lambda x)\]with \(x \geq 0\). Here, \(I_\nu (x)\) is the modified Bessel function of the first kind.
Examples
>>> from sympy.stats import ChiNoncentral, density >>> from sympy import Symbol
>>> k = Symbol("k", integer=True) >>> l = Symbol("l") >>> z = Symbol("z")
>>> X = ChiNoncentral("x", k, l)
>>> density(X)(z) l*z**k*exp(-l**2/2 - z**2/2)*besseli(k/2 - 1, l*z)/(l*z)**(k/2)
References
- sympy.stats.ChiSquared(name, k)[source]¶
Create a continuous random variable with a Chi-squared distribution.
- Parameters
k : Positive integer
The number of degrees of freedom.
- Returns
RandomSymbol
Explanation
The density of the Chi-squared distribution is given by
\[f(x) := \frac{1}{2^{\frac{k}{2}}\Gamma\left(\frac{k}{2}\right)} x^{\frac{k}{2}-1} e^{-\frac{x}{2}}\]with \(x \geq 0\).
Examples
>>> from sympy.stats import ChiSquared, density, E, variance, moment >>> from sympy import Symbol
>>> k = Symbol("k", integer=True, positive=True) >>> z = Symbol("z")
>>> X = ChiSquared("x", k)
>>> density(X)(z) z**(k/2 - 1)*exp(-z/2)/(2**(k/2)*gamma(k/2))
>>> E(X) k
>>> variance(X) 2*k
>>> moment(X, 3) k**3 + 6*k**2 + 8*k
References
- sympy.stats.Dagum(name, p, a, b)[source]¶
Create a continuous random variable with a Dagum distribution.
- Parameters
p : Real number
\(p > 0\), a shape.
a : Real number
\(a > 0\), a shape.
b : Real number
\(b > 0\), a scale.
- Returns
RandomSymbol
Explanation
The density of the Dagum distribution is given by
\[f(x) := \frac{a p}{x} \left( \frac{\left(\tfrac{x}{b}\right)^{a p}} {\left(\left(\tfrac{x}{b}\right)^a + 1 \right)^{p+1}} \right)\]with \(x > 0\).
Examples
>>> from sympy.stats import Dagum, density, cdf >>> from sympy import Symbol
>>> p = Symbol("p", positive=True) >>> a = Symbol("a", positive=True) >>> b = Symbol("b", positive=True) >>> z = Symbol("z")
>>> X = Dagum("x", p, a, b)
>>> density(X)(z) a*p*(z/b)**(a*p)*((z/b)**a + 1)**(-p - 1)/z
>>> cdf(X)(z) Piecewise(((1 + (z/b)**(-a))**(-p), z >= 0), (0, True))
References
- sympy.stats.Erlang(name, k, l)[source]¶
Create a continuous random variable with an Erlang distribution.
- Parameters
k : Positive integer
l : Real number, \(\lambda > 0\), the rate
- Returns
RandomSymbol
Explanation
The density of the Erlang distribution is given by
\[f(x) := \frac{\lambda^k x^{k-1} e^{-\lambda x}}{(k-1)!}\]with \(x \in [0,\infty]\).
Examples
>>> from sympy.stats import Erlang, density, cdf, E, variance >>> from sympy import Symbol, simplify, pprint
>>> k = Symbol("k", integer=True, positive=True) >>> l = Symbol("l", positive=True) >>> z = Symbol("z")
>>> X = Erlang("x", k, l)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) k k - 1 -l*z l *z *e --------------- Gamma(k)
>>> C = cdf(X)(z) >>> pprint(C, use_unicode=False) /lowergamma(k, l*z) |------------------ for z > 0 < Gamma(k) | \ 0 otherwise
>>> E(X) k/l
>>> simplify(variance(X)) k/l**2
References
- sympy.stats.ExGaussian(name, mean, std, rate)[source]¶
Create a continuous random variable with an Exponentially modified Gaussian (EMG) distribution.
- Parameters
mu : A Real number, the mean of Gaussian component
std: A positive Real number,
- math
\(\sigma^2 > 0\) the variance of Gaussian component
lambda: A positive Real number,
- math
\(\lambda > 0\) the rate of Exponential component
- Returns
RandomSymbol
Explanation
The density of the exponentially modified Gaussian distribution is given by
\[f(x) := \frac{\lambda}{2}e^{\frac{\lambda}{2}(2\mu+\lambda\sigma^2-2x)} \text{erfc}(\frac{\mu + \lambda\sigma^2 - x}{\sqrt{2}\sigma})\]with \(x > 0\). Note that the expected value is \(1/\lambda\).
Examples
>>> from sympy.stats import ExGaussian, density, cdf, E >>> from sympy.stats import variance, skewness >>> from sympy import Symbol, pprint, simplify
>>> mean = Symbol("mu") >>> std = Symbol("sigma", positive=True) >>> rate = Symbol("lamda", positive=True) >>> z = Symbol("z") >>> X = ExGaussian("x", mean, std, rate)
>>> pprint(density(X)(z), use_unicode=False) / 2 \ lamda*\lamda*sigma + 2*mu - 2*z/ --------------------------------- / ___ / 2 \\ 2 |\/ 2 *\lamda*sigma + mu - z/| lamda*e *erfc|-----------------------------| \ 2*sigma / ---------------------------------------------------------------------------- 2
>>> cdf(X)(z) -(erf(sqrt(2)*(-lamda**2*sigma**2 + lamda*(-mu + z))/(2*lamda*sigma))/2 + 1/2)*exp(lamda**2*sigma**2/2 - lamda*(-mu + z)) + erf(sqrt(2)*(-mu + z)/(2*sigma))/2 + 1/2
>>> E(X) (lamda*mu + 1)/lamda
>>> simplify(variance(X)) sigma**2 + lamda**(-2)
>>> simplify(skewness(X)) 2/(lamda**2*sigma**2 + 1)**(3/2)
References
- sympy.stats.Exponential(name, rate)[source]¶
Create a continuous random variable with an Exponential distribution.
- Parameters
rate : A positive Real number, \(\lambda > 0\), the rate (or inverse scale/inverse mean)
- Returns
RandomSymbol
Explanation
The density of the exponential distribution is given by
\[f(x) := \lambda \exp(-\lambda x)\]with \(x > 0\). Note that the expected value is \(1/\lambda\).
Examples
>>> from sympy.stats import Exponential, density, cdf, E >>> from sympy.stats import variance, std, skewness, quantile >>> from sympy import Symbol
>>> l = Symbol("lambda", positive=True) >>> z = Symbol("z") >>> p = Symbol("p") >>> X = Exponential("x", l)
>>> density(X)(z) lambda*exp(-lambda*z)
>>> cdf(X)(z) Piecewise((1 - exp(-lambda*z), z >= 0), (0, True))
>>> quantile(X)(p) -log(1 - p)/lambda
>>> E(X) 1/lambda
>>> variance(X) lambda**(-2)
>>> skewness(X) 2
>>> X = Exponential('x', 10)
>>> density(X)(z) 10*exp(-10*z)
>>> E(X) 1/10
>>> std(X) 1/10
References
- sympy.stats.FDistribution(name, d1, d2)[source]¶
Create a continuous random variable with a F distribution.
- Parameters
d1 : \(d_1 > 0\), where \(d_1\) is the degrees of freedom (\(n_1 - 1\))
d2 : \(d_2 > 0\), where \(d_2\) is the degrees of freedom (\(n_2 - 1\))
- Returns
RandomSymbol
Explanation
The density of the F distribution is given by
\[f(x) := \frac{\sqrt{\frac{(d_1 x)^{d_1} d_2^{d_2}} {(d_1 x + d_2)^{d_1 + d_2}}}} {x \mathrm{B} \left(\frac{d_1}{2}, \frac{d_2}{2}\right)}\]with \(x > 0\).
Examples
>>> from sympy.stats import FDistribution, density >>> from sympy import Symbol, pprint
>>> d1 = Symbol("d1", positive=True) >>> d2 = Symbol("d2", positive=True) >>> z = Symbol("z")
>>> X = FDistribution("x", d1, d2)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) d2 -- ______________________________ 2 / d1 -d1 - d2 d2 *\/ (d1*z) *(d1*z + d2) -------------------------------------- /d1 d2\ z*B|--, --| \2 2 /
References
- sympy.stats.FisherZ(name, d1, d2)[source]¶
Create a Continuous Random Variable with an Fisher’s Z distribution.
- Parameters
d1 : \(d_1 > 0\)
Degree of freedom.
d2 : \(d_2 > 0\)
Degree of freedom.
- Returns
RandomSymbol
Explanation
The density of the Fisher’s Z distribution is given by
\[f(x) := \frac{2d_1^{d_1/2} d_2^{d_2/2}} {\mathrm{B}(d_1/2, d_2/2)} \frac{e^{d_1z}}{\left(d_1e^{2z}+d_2\right)^{\left(d_1+d_2\right)/2}}\]Examples
>>> from sympy.stats import FisherZ, density >>> from sympy import Symbol, pprint
>>> d1 = Symbol("d1", positive=True) >>> d2 = Symbol("d2", positive=True) >>> z = Symbol("z")
>>> X = FisherZ("x", d1, d2)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) d1 d2 d1 d2 - -- - -- -- -- 2 2 2 2 / 2*z \ d1*z 2*d1 *d2 *\d1*e + d2/ *e ----------------------------------------- /d1 d2\ B|--, --| \2 2 /
References
- sympy.stats.Frechet(name, a, s=1, m=0)[source]¶
Create a continuous random variable with a Frechet distribution.
- Parameters
a : Real number, \(a \in \left(0, \infty\right)\) the shape
s : Real number, \(s \in \left(0, \infty\right)\) the scale
m : Real number, \(m \in \left(-\infty, \infty\right)\) the minimum
- Returns
RandomSymbol
Explanation
The density of the Frechet distribution is given by
\[f(x) := \frac{\alpha}{s} \left(\frac{x-m}{s}\right)^{-1-\alpha} e^{-(\frac{x-m}{s})^{-\alpha}}\]with \(x \geq m\).
Examples
>>> from sympy.stats import Frechet, density, cdf >>> from sympy import Symbol
>>> a = Symbol("a", positive=True) >>> s = Symbol("s", positive=True) >>> m = Symbol("m", real=True) >>> z = Symbol("z")
>>> X = Frechet("x", a, s, m)
>>> density(X)(z) a*((-m + z)/s)**(-a - 1)*exp(-1/((-m + z)/s)**a)/s
>>> cdf(X)(z) Piecewise((exp(-1/((-m + z)/s)**a), m <= z), (0, True))
References
- sympy.stats.Gamma(name, k, theta)[source]¶
Create a continuous random variable with a Gamma distribution.
- Parameters
k : Real number, \(k > 0\), a shape
theta : Real number, \(\theta > 0\), a scale
- Returns
RandomSymbol
Explanation
The density of the Gamma distribution is given by
\[f(x) := \frac{1}{\Gamma(k) \theta^k} x^{k - 1} e^{-\frac{x}{\theta}}\]with \(x \in [0,1]\).
Examples
>>> from sympy.stats import Gamma, density, cdf, E, variance >>> from sympy import Symbol, pprint, simplify
>>> k = Symbol("k", positive=True) >>> theta = Symbol("theta", positive=True) >>> z = Symbol("z")
>>> X = Gamma("x", k, theta)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) -z ----- -k k - 1 theta theta *z *e --------------------- Gamma(k)
>>> C = cdf(X, meijerg=True)(z) >>> pprint(C, use_unicode=False) / / z \ |k*lowergamma|k, -----| | \ theta/ <---------------------- for z >= 0 | Gamma(k + 1) | \ 0 otherwise
>>> E(X) k*theta
>>> V = simplify(variance(X)) >>> pprint(V, use_unicode=False) 2 k*theta
References
- sympy.stats.GammaInverse(name, a, b)[source]¶
Create a continuous random variable with an inverse Gamma distribution.
- Parameters
a : Real number, \(a > 0\), a shape
b : Real number, \(b > 0\), a scale
- Returns
RandomSymbol
Explanation
The density of the inverse Gamma distribution is given by
\[f(x) := \frac{\beta^\alpha}{\Gamma(\alpha)} x^{-\alpha - 1} \exp\left(\frac{-\beta}{x}\right)\]with \(x > 0\).
Examples
>>> from sympy.stats import GammaInverse, density, cdf >>> from sympy import Symbol, pprint
>>> a = Symbol("a", positive=True) >>> b = Symbol("b", positive=True) >>> z = Symbol("z")
>>> X = GammaInverse("x", a, b)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) -b --- a -a - 1 z b *z *e --------------- Gamma(a)
>>> cdf(X)(z) Piecewise((uppergamma(a, b/z)/gamma(a), z > 0), (0, True))
References
- sympy.stats.Gompertz(name, b, eta)[source]¶
Create a Continuous Random Variable with Gompertz distribution.
- Parameters
b: Real number, ‘b > 0’, a scale
eta: Real number, ‘eta > 0’, a shape
- Returns
RandomSymbol
Explanation
The density of the Gompertz distribution is given by
\[f(x) := b \eta e^{b x} e^{\eta} \exp \left(-\eta e^{bx} \right)\]with :math: ‘x in [0, inf)’.
Examples
>>> from sympy.stats import Gompertz, density >>> from sympy import Symbol
>>> b = Symbol("b", positive=True) >>> eta = Symbol("eta", positive=True) >>> z = Symbol("z")
>>> X = Gompertz("x", b, eta)
>>> density(X)(z) b*eta*exp(eta)*exp(b*z)*exp(-eta*exp(b*z))
References
- sympy.stats.Gumbel(name, beta, mu, minimum=False)[source]¶
Create a Continuous Random Variable with Gumbel distribution.
- Parameters
mu : Real number, ‘mu’, a location
beta : Real number, ‘beta > 0’, a scale
minimum : Boolean, by default
False, set toTruefor enabling minimum distribution- Returns
RandomSymbol
Explanation
The density of the Gumbel distribution is given by
For Maximum
\[f(x) := \dfrac{1}{\beta} \exp \left( -\dfrac{x-\mu}{\beta} - \exp \left( -\dfrac{x - \mu}{\beta} \right) \right)\]with \(x \in [ - \infty, \infty ]\).
For Minimum
\[f(x) := \frac{e^{- e^{\frac{- \mu + x}{\beta}} + \frac{- \mu + x}{\beta}}}{\beta}\]with \(x \in [ - \infty, \infty ]\).
Examples
>>> from sympy.stats import Gumbel, density, cdf >>> from sympy import Symbol >>> x = Symbol("x") >>> mu = Symbol("mu") >>> beta = Symbol("beta", positive=True) >>> X = Gumbel("x", beta, mu) >>> density(X)(x) exp(-exp(-(-mu + x)/beta) - (-mu + x)/beta)/beta >>> cdf(X)(x) exp(-exp(-(-mu + x)/beta))
References
- sympy.stats.Kumaraswamy(name, a, b)[source]¶
Create a Continuous Random Variable with a Kumaraswamy distribution.
- Parameters
a : Real number, \(a > 0\), a shape
b : Real number, \(b > 0\), a shape
- Returns
RandomSymbol
Explanation
The density of the Kumaraswamy distribution is given by
\[f(x) := a b x^{a-1} (1-x^a)^{b-1}\]with \(x \in [0,1]\).
Examples
>>> from sympy.stats import Kumaraswamy, density, cdf >>> from sympy import Symbol, pprint
>>> a = Symbol("a", positive=True) >>> b = Symbol("b", positive=True) >>> z = Symbol("z")
>>> X = Kumaraswamy("x", a, b)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) b - 1 a - 1 / a\ a*b*z *\1 - z /
>>> cdf(X)(z) Piecewise((0, z < 0), (1 - (1 - z**a)**b, z <= 1), (1, True))
References
- sympy.stats.Laplace(name, mu, b)[source]¶
Create a continuous random variable with a Laplace distribution.
- Parameters
mu : Real number or a list/matrix, the location (mean) or the
location vector
b : Real number or a positive definite matrix, representing a scale
or the covariance matrix.
- Returns
RandomSymbol
Explanation
The density of the Laplace distribution is given by
\[f(x) := \frac{1}{2 b} \exp \left(-\frac{|x-\mu|}b \right)\]Examples
>>> from sympy.stats import Laplace, density, cdf >>> from sympy import Symbol, pprint
>>> mu = Symbol("mu") >>> b = Symbol("b", positive=True) >>> z = Symbol("z")
>>> X = Laplace("x", mu, b)
>>> density(X)(z) exp(-Abs(mu - z)/b)/(2*b)
>>> cdf(X)(z) Piecewise((exp((-mu + z)/b)/2, mu > z), (1 - exp((mu - z)/b)/2, True))
>>> L = Laplace('L', [1, 2], [[1, 0], [0, 1]]) >>> pprint(density(L)(1, 2), use_unicode=False) 5 / ____\ e *besselk\0, \/ 35 / --------------------- pi
References
- sympy.stats.Levy(name, mu, c)[source]¶
Create a continuous random variable with a Levy distribution.
The density of the Levy distribution is given by
\[f(x) := \sqrt(\frac{c}{2 \pi}) \frac{\exp -\frac{c}{2 (x - \mu)}}{(x - \mu)^{3/2}}\]- Parameters
mu : Real number
The location parameter.
c : Real number, \(c > 0\)
A scale parameter.
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Levy, density, cdf >>> from sympy import Symbol
>>> mu = Symbol("mu", real=True) >>> c = Symbol("c", positive=True) >>> z = Symbol("z")
>>> X = Levy("x", mu, c)
>>> density(X)(z) sqrt(2)*sqrt(c)*exp(-c/(-2*mu + 2*z))/(2*sqrt(pi)*(-mu + z)**(3/2))
>>> cdf(X)(z) erfc(sqrt(c)*sqrt(1/(-2*mu + 2*z)))
References
- sympy.stats.Logistic(name, mu, s)[source]¶
Create a continuous random variable with a logistic distribution.
- Parameters
mu : Real number, the location (mean)
s : Real number, \(s > 0\), a scale
- Returns
RandomSymbol
Explanation
The density of the logistic distribution is given by
\[f(x) := \frac{e^{-(x-\mu)/s}} {s\left(1+e^{-(x-\mu)/s}\right)^2}\]Examples
>>> from sympy.stats import Logistic, density, cdf >>> from sympy import Symbol
>>> mu = Symbol("mu", real=True) >>> s = Symbol("s", positive=True) >>> z = Symbol("z")
>>> X = Logistic("x", mu, s)
>>> density(X)(z) exp((mu - z)/s)/(s*(exp((mu - z)/s) + 1)**2)
>>> cdf(X)(z) 1/(exp((mu - z)/s) + 1)
References
- sympy.stats.LogLogistic(name, alpha, beta)[source]¶
Create a continuous random variable with a log-logistic distribution. The distribution is unimodal when
beta > 1.- Parameters
alpha : Real number, \(\alpha > 0\), scale parameter and median of distribution
beta : Real number, \(\beta > 0\), a shape parameter
- Returns
RandomSymbol
Explanation
The density of the log-logistic distribution is given by
\[f(x) := \frac{(\frac{\beta}{\alpha})(\frac{x}{\alpha})^{\beta - 1}} {(1 + (\frac{x}{\alpha})^{\beta})^2}\]Examples
>>> from sympy.stats import LogLogistic, density, cdf, quantile >>> from sympy import Symbol, pprint
>>> alpha = Symbol("alpha", positive=True) >>> beta = Symbol("beta", positive=True) >>> p = Symbol("p") >>> z = Symbol("z", positive=True)
>>> X = LogLogistic("x", alpha, beta)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) beta - 1 / z \ beta*|-----| \alpha/ ------------------------ 2 / beta \ |/ z \ | alpha*||-----| + 1| \\alpha/ /
>>> cdf(X)(z) 1/(1 + (z/alpha)**(-beta))
>>> quantile(X)(p) alpha*(p/(1 - p))**(1/beta)
References
- sympy.stats.LogNormal(name, mean, std)[source]¶
Create a continuous random variable with a log-normal distribution.
- Parameters
mu : Real number
The log-scale.
sigma : Real number
A shape. (\(\sigma^2 > 0\))
- Returns
RandomSymbol
Explanation
The density of the log-normal distribution is given by
\[f(x) := \frac{1}{x\sqrt{2\pi\sigma^2}} e^{-\frac{\left(\ln x-\mu\right)^2}{2\sigma^2}}\]with \(x \geq 0\).
Examples
>>> from sympy.stats import LogNormal, density >>> from sympy import Symbol, pprint
>>> mu = Symbol("mu", real=True) >>> sigma = Symbol("sigma", positive=True) >>> z = Symbol("z")
>>> X = LogNormal("x", mu, sigma)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) 2 -(-mu + log(z)) ----------------- 2 ___ 2*sigma \/ 2 *e ------------------------ ____ 2*\/ pi *sigma*z
>>> X = LogNormal('x', 0, 1) # Mean 0, standard deviation 1
>>> density(X)(z) sqrt(2)*exp(-log(z)**2/2)/(2*sqrt(pi)*z)
References
- sympy.stats.Lomax(name, alpha, lamda)[source]¶
Create a continuous random variable with a Lomax distribution.
- Parameters
alpha : Real Number, \(\alpha > 0\)
Shape parameter
lamda : Real Number, \(\lambda > 0\)
Scale parameter
- Returns
RandomSymbol
Explanation
The density of the Lomax distribution is given by
\[f(x) := \frac{\alpha}{\lambda}\left[1+\frac{x}{\lambda}\right]^{-(\alpha+1)}\]Examples
>>> from sympy.stats import Lomax, density, cdf, E >>> from sympy import symbols >>> a, l = symbols('a, l', positive=True) >>> X = Lomax('X', a, l) >>> x = symbols('x') >>> density(X)(x) a*(1 + x/l)**(-a - 1)/l >>> cdf(X)(x) Piecewise((1 - 1/(1 + x/l)**a, x >= 0), (0, True)) >>> a = 2 >>> X = Lomax('X', a, l) >>> E(X) l
References
- sympy.stats.Maxwell(name, a)[source]¶
Create a continuous random variable with a Maxwell distribution.
- Parameters
a : Real number, \(a > 0\)
- Returns
RandomSymbol
Explanation
The density of the Maxwell distribution is given by
\[f(x) := \sqrt{\frac{2}{\pi}} \frac{x^2 e^{-x^2/(2a^2)}}{a^3}\]with \(x \geq 0\).
Examples
>>> from sympy.stats import Maxwell, density, E, variance >>> from sympy import Symbol, simplify
>>> a = Symbol("a", positive=True) >>> z = Symbol("z")
>>> X = Maxwell("x", a)
>>> density(X)(z) sqrt(2)*z**2*exp(-z**2/(2*a**2))/(sqrt(pi)*a**3)
>>> E(X) 2*sqrt(2)*a/sqrt(pi)
>>> simplify(variance(X)) a**2*(-8 + 3*pi)/pi
References
- sympy.stats.Moyal(name, mu, sigma)[source]¶
Create a continuous random variable with a Moyal distribution.
- Parameters
mu : Real number
Location parameter
sigma : Real positive number
Scale parameter
- Returns
RandomSymbol
Explanation
The density of the Moyal distribution is given by
\[f(x) := \frac{\exp-\frac{1}{2}\exp-\frac{x-\mu}{\sigma}-\frac{x-\mu}{2\sigma}}{\sqrt{2\pi}\sigma}\]with \(x \in \mathbb{R}\).
Examples
>>> from sympy.stats import Moyal, density, cdf >>> from sympy import Symbol, simplify >>> mu = Symbol("mu", real=True) >>> sigma = Symbol("sigma", positive=True, real=True) >>> z = Symbol("z") >>> X = Moyal("x", mu, sigma) >>> density(X)(z) sqrt(2)*exp(-exp((mu - z)/sigma)/2 - (-mu + z)/(2*sigma))/(2*sqrt(pi)*sigma) >>> simplify(cdf(X)(z)) 1 - erf(sqrt(2)*exp((mu - z)/(2*sigma))/2)
References
- sympy.stats.Nakagami(name, mu, omega)[source]¶
Create a continuous random variable with a Nakagami distribution.
- Parameters
mu : Real number, \(\mu \geq \frac{1}{2}\), a shape
omega : Real number, \(\omega > 0\), the spread
- Returns
RandomSymbol
Explanation
The density of the Nakagami distribution is given by
\[f(x) := \frac{2\mu^\mu}{\Gamma(\mu)\omega^\mu} x^{2\mu-1} \exp\left(-\frac{\mu}{\omega}x^2 \right)\]with \(x > 0\).
Examples
>>> from sympy.stats import Nakagami, density, E, variance, cdf >>> from sympy import Symbol, simplify, pprint
>>> mu = Symbol("mu", positive=True) >>> omega = Symbol("omega", positive=True) >>> z = Symbol("z")
>>> X = Nakagami("x", mu, omega)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) 2 -mu*z ------- mu -mu 2*mu - 1 omega 2*mu *omega *z *e ---------------------------------- Gamma(mu)
>>> simplify(E(X)) sqrt(mu)*sqrt(omega)*gamma(mu + 1/2)/gamma(mu + 1)
>>> V = simplify(variance(X)) >>> pprint(V, use_unicode=False) 2 omega*Gamma (mu + 1/2) omega - ----------------------- Gamma(mu)*Gamma(mu + 1)
>>> cdf(X)(z) Piecewise((lowergamma(mu, mu*z**2/omega)/gamma(mu), z > 0), (0, True))
References
- sympy.stats.Normal(name, mean, std)[source]¶
Create a continuous random variable with a Normal distribution.
- Parameters
mu : Real number or a list representing the mean or the mean vector
sigma : Real number or a positive definite square matrix,
\(\sigma^2 > 0\), the variance
- Returns
RandomSymbol
Explanation
The density of the Normal distribution is given by
\[f(x) := \frac{1}{\sigma\sqrt{2\pi}} e^{ -\frac{(x-\mu)^2}{2\sigma^2} }\]Examples
>>> from sympy.stats import Normal, density, E, std, cdf, skewness, quantile, marginal_distribution >>> from sympy import Symbol, simplify, pprint
>>> mu = Symbol("mu") >>> sigma = Symbol("sigma", positive=True) >>> z = Symbol("z") >>> y = Symbol("y") >>> p = Symbol("p") >>> X = Normal("x", mu, sigma)
>>> density(X)(z) sqrt(2)*exp(-(-mu + z)**2/(2*sigma**2))/(2*sqrt(pi)*sigma)
>>> C = simplify(cdf(X))(z) # it needs a little more help... >>> pprint(C, use_unicode=False) / ___ \ |\/ 2 *(-mu + z)| erf|---------------| \ 2*sigma / 1 -------------------- + - 2 2
>>> quantile(X)(p) mu + sqrt(2)*sigma*erfinv(2*p - 1)
>>> simplify(skewness(X)) 0
>>> X = Normal("x", 0, 1) # Mean 0, standard deviation 1 >>> density(X)(z) sqrt(2)*exp(-z**2/2)/(2*sqrt(pi))
>>> E(2*X + 1) 1
>>> simplify(std(2*X + 1)) 2
>>> m = Normal('X', [1, 2], [[2, 1], [1, 2]]) >>> pprint(density(m)(y, z), use_unicode=False) 2 2 y y*z z - -- + --- - -- + z - 1 ___ 3 3 3 \/ 3 *e ------------------------------ 6*pi
>>> marginal_distribution(m, m[0])(1) 1/(2*sqrt(pi))
References
- sympy.stats.Pareto(name, xm, alpha)[source]¶
Create a continuous random variable with the Pareto distribution.
- Parameters
xm : Real number, \(x_m > 0\), a scale
alpha : Real number, \(\alpha > 0\), a shape
- Returns
RandomSymbol
Explanation
The density of the Pareto distribution is given by
\[f(x) := \frac{\alpha\,x_m^\alpha}{x^{\alpha+1}}\]with \(x \in [x_m,\infty]\).
Examples
>>> from sympy.stats import Pareto, density >>> from sympy import Symbol
>>> xm = Symbol("xm", positive=True) >>> beta = Symbol("beta", positive=True) >>> z = Symbol("z")
>>> X = Pareto("x", xm, beta)
>>> density(X)(z) beta*xm**beta*z**(-beta - 1)
References
- sympy.stats.PowerFunction(name, alpha, a, b)[source]¶
Creates a continuous random variable with a Power Function Distribution.
- Parameters
alpha: Positive number, `0 < alpha`, the shape paramater
a : Real number, \(-\infty < a\), the left boundary
b : Real number, \(a < b < \infty\), the right boundary
- Returns
RandomSymbol
Explanation
The density of PowerFunction distribution is given by
\[f(x) := \frac{{\alpha}(x - a)^{\alpha - 1}}{(b - a)^{\alpha}}\]with \(x \in [a,b]\).
Examples
>>> from sympy.stats import PowerFunction, density, cdf, E, variance >>> from sympy import Symbol >>> alpha = Symbol("alpha", positive=True) >>> a = Symbol("a", real=True) >>> b = Symbol("b", real=True) >>> z = Symbol("z")
>>> X = PowerFunction("X", 2, a, b)
>>> density(X)(z) (-2*a + 2*z)/(-a + b)**2
>>> cdf(X)(z) Piecewise((a**2/(a**2 - 2*a*b + b**2) - 2*a*z/(a**2 - 2*a*b + b**2) + z**2/(a**2 - 2*a*b + b**2), a <= z), (0, True))
>>> alpha = 2 >>> a = 0 >>> b = 1 >>> Y = PowerFunction("Y", alpha, a, b)
>>> E(Y) 2/3
>>> variance(Y) 1/18
References
- sympy.stats.QuadraticU(name, a, b)[source]¶
Create a Continuous Random Variable with a U-quadratic distribution.
- Parameters
a : Real number
b : Real number, \(a < b\)
- Returns
RandomSymbol
Explanation
The density of the U-quadratic distribution is given by
\[f(x) := \alpha (x-\beta)^2\]with \(x \in [a,b]\).
Examples
>>> from sympy.stats import QuadraticU, density >>> from sympy import Symbol, pprint
>>> a = Symbol("a", real=True) >>> b = Symbol("b", real=True) >>> z = Symbol("z")
>>> X = QuadraticU("x", a, b)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) / 2 | / a b \ |12*|- - - - + z| | \ 2 2 / <----------------- for And(b >= z, a <= z) | 3 | (-a + b) | \ 0 otherwise
References
- sympy.stats.RaisedCosine(name, mu, s)[source]¶
Create a Continuous Random Variable with a raised cosine distribution.
- Parameters
mu : Real number
s : Real number, \(s > 0\)
- Returns
RandomSymbol
Explanation
The density of the raised cosine distribution is given by
\[f(x) := \frac{1}{2s}\left(1+\cos\left(\frac{x-\mu}{s}\pi\right)\right)\]with \(x \in [\mu-s,\mu+s]\).
Examples
>>> from sympy.stats import RaisedCosine, density >>> from sympy import Symbol, pprint
>>> mu = Symbol("mu", real=True) >>> s = Symbol("s", positive=True) >>> z = Symbol("z")
>>> X = RaisedCosine("x", mu, s)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) / /pi*(-mu + z)\ |cos|------------| + 1 | \ s / <--------------------- for And(z >= mu - s, z <= mu + s) | 2*s | \ 0 otherwise
References
- sympy.stats.Rayleigh(name, sigma)[source]¶
Create a continuous random variable with a Rayleigh distribution.
- Parameters
sigma : Real number, \(\sigma > 0\)
- Returns
RandomSymbol
Explanation
The density of the Rayleigh distribution is given by
\[f(x) := \frac{x}{\sigma^2} e^{-x^2/2\sigma^2}\]with \(x > 0\).
Examples
>>> from sympy.stats import Rayleigh, density, E, variance >>> from sympy import Symbol
>>> sigma = Symbol("sigma", positive=True) >>> z = Symbol("z")
>>> X = Rayleigh("x", sigma)
>>> density(X)(z) z*exp(-z**2/(2*sigma**2))/sigma**2
>>> E(X) sqrt(2)*sqrt(pi)*sigma/2
>>> variance(X) -pi*sigma**2/2 + 2*sigma**2
References
- sympy.stats.Reciprocal(name, a, b)[source]¶
Creates a continuous random variable with a reciprocal distribution.
- Parameters
a : Real number, \(0 < a\)
b : Real number, \(a < b\)
- Returns
RandomSymbol
Examples
>>> from sympy.stats import Reciprocal, density, cdf >>> from sympy import symbols >>> a, b, x = symbols('a, b, x', positive=True) >>> R = Reciprocal('R', a, b)
>>> density(R)(x) 1/(x*(-log(a) + log(b))) >>> cdf(R)(x) Piecewise((log(a)/(log(a) - log(b)) - log(x)/(log(a) - log(b)), a <= x), (0, True))
Reference
- sympy.stats.StudentT(name, nu)[source]¶
Create a continuous random variable with a student’s t distribution.
- Parameters
nu : Real number, \(\nu > 0\), the degrees of freedom
- Returns
RandomSymbol
Explanation
The density of the student’s t distribution is given by
\[f(x) := \frac{\Gamma \left(\frac{\nu+1}{2} \right)} {\sqrt{\nu\pi}\Gamma \left(\frac{\nu}{2} \right)} \left(1+\frac{x^2}{\nu} \right)^{-\frac{\nu+1}{2}}\]Examples
>>> from sympy.stats import StudentT, density, cdf >>> from sympy import Symbol, pprint
>>> nu = Symbol("nu", positive=True) >>> z = Symbol("z")
>>> X = StudentT("x", nu)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) nu 1 - -- - - 2 2 / 2\ | z | |1 + --| \ nu/ ----------------- ____ / nu\ \/ nu *B|1/2, --| \ 2 /
>>> cdf(X)(z) 1/2 + z*gamma(nu/2 + 1/2)*hyper((1/2, nu/2 + 1/2), (3/2,), -z**2/nu)/(sqrt(pi)*sqrt(nu)*gamma(nu/2))
References
- sympy.stats.ShiftedGompertz(name, b, eta)[source]¶
Create a continuous random variable with a Shifted Gompertz distribution.
- Parameters
b: Real number, ‘b > 0’, a scale
eta: Real number, ‘eta > 0’, a shape
- Returns
RandomSymbol
Explanation
The density of the Shifted Gompertz distribution is given by
\[f(x) := b e^{-b x} e^{-\eta \exp(-b x)} \left[1 + \eta(1 - e^(-bx)) \right]\]with :math: ‘x in [0, inf)’.
Examples
>>> from sympy.stats import ShiftedGompertz, density >>> from sympy import Symbol
>>> b = Symbol("b", positive=True) >>> eta = Symbol("eta", positive=True) >>> x = Symbol("x")
>>> X = ShiftedGompertz("x", b, eta)
>>> density(X)(x) b*(eta*(1 - exp(-b*x)) + 1)*exp(-b*x)*exp(-eta*exp(-b*x))
References
- sympy.stats.Trapezoidal(name, a, b, c, d)[source]¶
Create a continuous random variable with a trapezoidal distribution.
- Parameters
a : Real number, \(a < d\)
b : Real number, \(a \le b < c\)
c : Real number, \(b < c \le d\)
d : Real number
- Returns
RandomSymbol
Explanation
The density of the trapezoidal distribution is given by
\[\begin{split}f(x) := \begin{cases} 0 & \mathrm{for\ } x < a, \\ \frac{2(x-a)}{(b-a)(d+c-a-b)} & \mathrm{for\ } a \le x < b, \\ \frac{2}{d+c-a-b} & \mathrm{for\ } b \le x < c, \\ \frac{2(d-x)}{(d-c)(d+c-a-b)} & \mathrm{for\ } c \le x < d, \\ 0 & \mathrm{for\ } d < x. \end{cases}\end{split}\]Examples
>>> from sympy.stats import Trapezoidal, density >>> from sympy import Symbol, pprint
>>> a = Symbol("a") >>> b = Symbol("b") >>> c = Symbol("c") >>> d = Symbol("d") >>> z = Symbol("z")
>>> X = Trapezoidal("x", a,b,c,d)
>>> pprint(density(X)(z), use_unicode=False) / -2*a + 2*z |------------------------- for And(a <= z, b > z) |(-a + b)*(-a - b + c + d) | | 2 | -------------- for And(b <= z, c > z) < -a - b + c + d | | 2*d - 2*z |------------------------- for And(d >= z, c <= z) |(-c + d)*(-a - b + c + d) | \ 0 otherwise
References
- sympy.stats.Triangular(name, a, b, c)[source]¶
Create a continuous random variable with a triangular distribution.
- Parameters
a : Real number, \(a \in \left(-\infty, \infty\right)\)
b : Real number, \(a < b\)
c : Real number, \(a \leq c \leq b\)
- Returns
RandomSymbol
Explanation
The density of the triangular distribution is given by
\[\begin{split}f(x) := \begin{cases} 0 & \mathrm{for\ } x < a, \\ \frac{2(x-a)}{(b-a)(c-a)} & \mathrm{for\ } a \le x < c, \\ \frac{2}{b-a} & \mathrm{for\ } x = c, \\ \frac{2(b-x)}{(b-a)(b-c)} & \mathrm{for\ } c < x \le b, \\ 0 & \mathrm{for\ } b < x. \end{cases}\end{split}\]Examples
>>> from sympy.stats import Triangular, density >>> from sympy import Symbol, pprint
>>> a = Symbol("a") >>> b = Symbol("b") >>> c = Symbol("c") >>> z = Symbol("z")
>>> X = Triangular("x", a,b,c)
>>> pprint(density(X)(z), use_unicode=False) / -2*a + 2*z |----------------- for And(a <= z, c > z) |(-a + b)*(-a + c) | | 2 | ------ for c = z < -a + b | | 2*b - 2*z |---------------- for And(b >= z, c < z) |(-a + b)*(b - c) | \ 0 otherwise
References
- sympy.stats.Uniform(name, left, right)[source]¶
Create a continuous random variable with a uniform distribution.
- Parameters
a : Real number, \(-\infty < a\), the left boundary
b : Real number, \(a < b < \infty\), the right boundary
- Returns
RandomSymbol
Explanation
The density of the uniform distribution is given by
\[\begin{split}f(x) := \begin{cases} \frac{1}{b - a} & \text{for } x \in [a,b] \\ 0 & \text{otherwise} \end{cases}\end{split}\]with \(x \in [a,b]\).
Examples
>>> from sympy.stats import Uniform, density, cdf, E, variance >>> from sympy import Symbol, simplify
>>> a = Symbol("a", negative=True) >>> b = Symbol("b", positive=True) >>> z = Symbol("z")
>>> X = Uniform("x", a, b)
>>> density(X)(z) Piecewise((1/(-a + b), (b >= z) & (a <= z)), (0, True))
>>> cdf(X)(z) Piecewise((0, a > z), ((-a + z)/(-a + b), b >= z), (1, True))
>>> E(X) a/2 + b/2
>>> simplify(variance(X)) a**2/12 - a*b/6 + b**2/12
References
- sympy.stats.UniformSum(name, n)[source]¶
Create a continuous random variable with an Irwin-Hall distribution.
- Parameters
n : A positive integer, \(n > 0\)
- Returns
RandomSymbol
Explanation
The probability distribution function depends on a single parameter \(n\) which is an integer.
The density of the Irwin-Hall distribution is given by
\[f(x) := \frac{1}{(n-1)!}\sum_{k=0}^{\left\lfloor x\right\rfloor}(-1)^k \binom{n}{k}(x-k)^{n-1}\]Examples
>>> from sympy.stats import UniformSum, density, cdf >>> from sympy import Symbol, pprint
>>> n = Symbol("n", integer=True) >>> z = Symbol("z")
>>> X = UniformSum("x", n)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) floor(z) ___ \ ` \ k n - 1 /n\ ) (-1) *(-k + z) *| | / \k/ /__, k = 0 -------------------------------- (n - 1)!
>>> cdf(X)(z) Piecewise((0, z < 0), (Sum((-1)**_k*(-_k + z)**n*binomial(n, _k), (_k, 0, floor(z)))/factorial(n), n >= z), (1, True))
Compute cdf with specific ‘x’ and ‘n’ values as follows : >>> cdf(UniformSum(“x”, 5), evaluate=False)(2).doit() 9/40
The argument evaluate=False prevents an attempt at evaluation of the sum for general n, before the argument 2 is passed.
References
- sympy.stats.VonMises(name, mu, k)[source]¶
Create a Continuous Random Variable with a von Mises distribution.
- Parameters
mu : Real number
Measure of location.
k : Real number
Measure of concentration.
- Returns
RandomSymbol
Explanation
The density of the von Mises distribution is given by
\[f(x) := \frac{e^{\kappa\cos(x-\mu)}}{2\pi I_0(\kappa)}\]with \(x \in [0,2\pi]\).
Examples
>>> from sympy.stats import VonMises, density >>> from sympy import Symbol, pprint
>>> mu = Symbol("mu") >>> k = Symbol("k", positive=True) >>> z = Symbol("z")
>>> X = VonMises("x", mu, k)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) k*cos(mu - z) e ------------------ 2*pi*besseli(0, k)
References
- sympy.stats.Wald(name, mean, shape)[source]¶
Create a continuous random variable with an Inverse Gaussian distribution. Inverse Gaussian distribution is also known as Wald distribution.
- Parameters
mu :
Positive number representing the mean.
lambda :
Positive number representing the shape parameter.
- Returns
RandomSymbol
Explanation
The density of the Inverse Gaussian distribution is given by
\[f(x) := \sqrt{\frac{\lambda}{2\pi x^3}} e^{-\frac{\lambda(x-\mu)^2}{2x\mu^2}}\]Examples
>>> from sympy.stats import GaussianInverse, density, E, std, skewness >>> from sympy import Symbol, pprint
>>> mu = Symbol("mu", positive=True) >>> lamda = Symbol("lambda", positive=True) >>> z = Symbol("z", positive=True) >>> X = GaussianInverse("x", mu, lamda)
>>> D = density(X)(z) >>> pprint(D, use_unicode=False) 2 -lambda*(-mu + z) ------------------- 2 ___ ________ 2*mu *z \/ 2 *\/ lambda *e ------------------------------------- ____ 3/2 2*\/ pi *z
>>> E(X) mu
>>> std(X).expand() mu**(3/2)/sqrt(lambda)
>>> skewness(X).expand() 3*sqrt(mu)/sqrt(lambda)
References
- sympy.stats.Weibull(name, alpha, beta)[source]¶
Create a continuous random variable with a Weibull distribution.
- Parameters
lambda : Real number, \(\lambda > 0\), a scale
k : Real number, \(k > 0\), a shape
- Returns
RandomSymbol
Explanation
The density of the Weibull distribution is given by
\[\begin{split}f(x) := \begin{cases} \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1} e^{-(x/\lambda)^{k}} & x\geq0\\ 0 & x<0 \end{cases}\end{split}\]Examples
>>> from sympy.stats import Weibull, density, E, variance >>> from sympy import Symbol, simplify
>>> l = Symbol("lambda", positive=True) >>> k = Symbol("k", positive=True) >>> z = Symbol("z")
>>> X = Weibull("x", l, k)
>>> density(X)(z) k*(z/lambda)**(k - 1)*exp(-(z/lambda)**k)/lambda
>>> simplify(E(X)) lambda*gamma(1 + 1/k)
>>> simplify(variance(X)) lambda**2*(-gamma(1 + 1/k)**2 + gamma(1 + 2/k))
References
- sympy.stats.WignerSemicircle(name, R)[source]¶
Create a continuous random variable with a Wigner semicircle distribution.
- Parameters
R : Real number, \(R > 0\), the radius
- Returns
A \(RandomSymbol\).
Explanation
The density of the Wigner semicircle distribution is given by
\[f(x) := \frac2{\pi R^2}\,\sqrt{R^2-x^2}\]with \(x \in [-R,R]\).
Examples
>>> from sympy.stats import WignerSemicircle, density, E >>> from sympy import Symbol
>>> R = Symbol("R", positive=True) >>> z = Symbol("z")
>>> X = WignerSemicircle("x", R)
>>> density(X)(z) 2*sqrt(R**2 - z**2)/(pi*R**2)
>>> E(X) 0
References
- sympy.stats.ContinuousRV(symbol, density, set=Interval(- oo, oo), **kwargs)[source]¶
Create a Continuous Random Variable given the following:
- Parameters
symbol : Symbol
Represents name of the random variable.
density : Expression containing symbol
Represents probability density function.
set : set/Interval
Represents the region where the pdf is valid, by default is real line.
check : bool
If True, it will check whether the given density integrates to 1 over the given set. If False, it will not perform this check. Default is False.
- Returns
RandomSymbol
Many common continuous random variable types are already implemented.
This function should be necessary only very rarely.
Examples
>>> from sympy import Symbol, sqrt, exp, pi >>> from sympy.stats import ContinuousRV, P, E
>>> x = Symbol("x")
>>> pdf = sqrt(2)*exp(-x**2/2)/(2*sqrt(pi)) # Normal distribution >>> X = ContinuousRV(x, pdf)
>>> E(X) 0 >>> P(X>0) 1/2
Joint Types¶
- sympy.stats.JointRV(symbol, pdf, _set=None)[source]¶
Create a Joint Random Variable where each of its component is continuous, given the following:
– a symbol – a PDF in terms of indexed symbols of the symbol given as the first argument
NOTE: As of now, the set for each component for a
JointRVis equal to the set of all integers, which cannot be changed.- Returns
RandomSymbol
Examples
>>> from sympy import exp, pi, Indexed, S >>> from sympy.stats import density, JointRV >>> x1, x2 = (Indexed('x', i) for i in (1, 2)) >>> pdf = exp(-x1**2/2 + x1 - x2**2/2 - S(1)/2)/(2*pi) >>> N1 = JointRV('x', pdf) #Multivariate Normal distribution >>> density(N1)(1, 2) exp(-2)/(2*pi)
- sympy.stats.marginal_distribution(rv, *indices)[source]¶
Marginal distribution function of a joint random variable.
- Parameters
rv: A random variable with a joint probability distribution.
indices: component indices or the indexed random symbol
for which the joint distribution is to be calculated
- Returns
A Lambda expression in \(sym\).
Examples
>>> from sympy.stats import MultivariateNormal, marginal_distribution >>> m = MultivariateNormal('X', [1, 2], [[2, 1], [1, 2]]) >>> marginal_distribution(m, m[0])(1) 1/(2*sqrt(pi))
- sympy.stats.MultivariateNormal(name, mu, sigma)[source]¶
Creates a continuous random variable with Multivariate Normal Distribution.
The density of the multivariate normal distribution can be found at [1].
- Parameters
mu : List representing the mean or the mean vector
sigma : Positive semidefinite square matrix
Represents covariance Matrix If \(\sigma\) is noninvertible then only sampling is supported currently
- Returns
RandomSymbol
Examples
>>> from sympy.stats import MultivariateNormal, density, marginal_distribution >>> from sympy import symbols, MatrixSymbol >>> X = MultivariateNormal('X', [3, 4], [[2, 1], [1, 2]]) >>> y, z = symbols('y z') >>> density(X)(y, z) sqrt(3)*exp(-y**2/3 + y*z/3 + 2*y/3 - z**2/3 + 5*z/3 - 13/3)/(6*pi) >>> density(X)(1, 2) sqrt(3)*exp(-4/3)/(6*pi) >>> marginal_distribution(X, X[1])(y) exp(-(y - 4)**2/4)/(2*sqrt(pi)) >>> marginal_distribution(X, X[0])(y) exp(-(y - 3)**2/4)/(2*sqrt(pi))
The example below shows that it is also possible to use symbolic parameters to define the MultivariateNormal class.
>>> n = symbols('n', integer=True, positive=True) >>> Sg = MatrixSymbol('Sg', n, n) >>> mu = MatrixSymbol('mu', n, 1) >>> obs = MatrixSymbol('obs', n, 1) >>> X = MultivariateNormal('X', mu, Sg)
The density of a multivariate normal can be calculated using a matrix argument, as shown below.
>>> density(X)(obs) (exp(((1/2)*mu.T - (1/2)*obs.T)*Sg**(-1)*(-mu + obs))/sqrt((2*pi)**n*Determinant(Sg)))[0, 0]
References
- sympy.stats.MultivariateLaplace(name, mu, sigma)[source]¶
Creates a continuous random variable with Multivariate Laplace Distribution.
The density of the multivariate Laplace distribution can be found at [1].
- Parameters
mu : List representing the mean or the mean vector
sigma : Positive definite square matrix
Represents covariance Matrix
- Returns
RandomSymbol
Examples
>>> from sympy.stats import MultivariateLaplace, density >>> from sympy import symbols >>> y, z = symbols('y z') >>> X = MultivariateLaplace('X', [2, 4], [[3, 1], [1, 3]]) >>> density(X)(y, z) sqrt(2)*exp(y/4 + 5*z/4)*besselk(0, sqrt(15*y*(3*y/8 - z/8)/2 + 15*z*(-y/8 + 3*z/8)/2))/(4*pi) >>> density(X)(1, 2) sqrt(2)*exp(11/4)*besselk(0, sqrt(165)/4)/(4*pi)
References
- sympy.stats.GeneralizedMultivariateLogGamma(syms, delta, v, lamda, mu)[source]¶
Creates a joint random variable with generalized multivariate log gamma distribution.
The joint pdf can be found at [1].
- Parameters
syms: list/tuple/set of symbols for identifying each component
delta: A constant in range $[0, 1]$
v: Positive real number
lamda: List of positive real numbers
mu: List of positive real numbers
- Returns
RandomSymbol
Examples
>>> from sympy.stats import density >>> from sympy.stats.joint_rv_types import GeneralizedMultivariateLogGamma >>> from sympy import symbols, S >>> v = 1 >>> l, mu = [1, 1, 1], [1, 1, 1] >>> d = S.Half >>> y = symbols('y_1:4', positive=True) >>> Gd = GeneralizedMultivariateLogGamma('G', d, v, l, mu) >>> density(Gd)(y[0], y[1], y[2]) Sum(exp((n + 1)*(y_1 + y_2 + y_3) - exp(y_1) - exp(y_2) - exp(y_3))/(2**n*gamma(n + 1)**3), (n, 0, oo))/2
Note
If the GeneralizedMultivariateLogGamma is too long to type use,
from sympy.stats.joint_rv_types import GeneralizedMultivariateLogGamma as GMVLGIf you want to pass the matrix omega instead of the constant delta, then use,GeneralizedMultivariateLogGammaOmega.References
- sympy.stats.GeneralizedMultivariateLogGammaOmega(syms, omega, v, lamda, mu)[source]¶
Extends GeneralizedMultivariateLogGamma.
- Parameters
syms: list/tuple/set of symbols
For identifying each component
omega: A square matrix
Every element of square matrix must be absolute value of square root of correlation coefficient
v: Positive real number
lamda: List of positive real numbers
mu: List of positive real numbers
- Returns
RandomSymbol
Examples
>>> from sympy.stats import density >>> from sympy.stats.joint_rv_types import GeneralizedMultivariateLogGammaOmega >>> from sympy import Matrix, symbols, S >>> omega = Matrix([[1, S.Half, S.Half], [S.Half, 1, S.Half], [S.Half, S.Half, 1]]) >>> v = 1 >>> l, mu = [1, 1, 1], [1, 1, 1] >>> G = GeneralizedMultivariateLogGammaOmega('G', omega, v, l, mu) >>> y = symbols('y_1:4', positive=True) >>> density(G)(y[0], y[1], y[2]) sqrt(2)*Sum((1 - sqrt(2)/2)**n*exp((n + 1)*(y_1 + y_2 + y_3) - exp(y_1) - exp(y_2) - exp(y_3))/gamma(n + 1)**3, (n, 0, oo))/2
Notes
If the GeneralizedMultivariateLogGammaOmega is too long to type use,
from sympy.stats.joint_rv_types import GeneralizedMultivariateLogGammaOmega as GMVLGOReferences
- sympy.stats.Multinomial(syms, n, *p)[source]¶
Creates a discrete random variable with Multinomial Distribution.
The density of the said distribution can be found at [1].
- Parameters
n: Positive integer
Represents number of trials
p: List of event probabilites
Must be in the range of [0, 1]
- Returns
RandomSymbol
Examples
>>> from sympy.stats import density, Multinomial, marginal_distribution >>> from sympy import symbols >>> x1, x2, x3 = symbols('x1, x2, x3', nonnegative=True, integer=True) >>> p1, p2, p3 = symbols('p1, p2, p3', positive=True) >>> M = Multinomial('M', 3, p1, p2, p3) >>> density(M)(x1, x2, x3) Piecewise((6*p1**x1*p2**x2*p3**x3/(factorial(x1)*factorial(x2)*factorial(x3)), Eq(x1 + x2 + x3, 3)), (0, True)) >>> marginal_distribution(M, M[0])(x1).subs(x1, 1) 3*p1*p2**2 + 6*p1*p2*p3 + 3*p1*p3**2
References
- sympy.stats.MultivariateBeta(syms, *alpha)[source]¶
Creates a continuous random variable with Dirichlet/Multivariate Beta Distribution.
The density of the Dirichlet distribution can be found at [1].
- Parameters
alpha: Positive real numbers
Signifies concentration numbers.
- Returns
RandomSymbol
Examples
>>> from sympy.stats import density, MultivariateBeta, marginal_distribution >>> from sympy import Symbol >>> a1 = Symbol('a1', positive=True) >>> a2 = Symbol('a2', positive=True) >>> B = MultivariateBeta('B', [a1, a2]) >>> C = MultivariateBeta('C', a1, a2) >>> x = Symbol('x') >>> y = Symbol('y') >>> density(B)(x, y) x**(a1 - 1)*y**(a2 - 1)*gamma(a1 + a2)/(gamma(a1)*gamma(a2)) >>> marginal_distribution(C, C[0])(x) x**(a1 - 1)*gamma(a1 + a2)/(a2*gamma(a1)*gamma(a2))
References
- sympy.stats.MultivariateEwens(syms, n, theta)[source]¶
Creates a discrete random variable with Multivariate Ewens Distribution.
The density of the said distribution can be found at [1].
- Parameters
n: Positive integer
Size of the sample or the integer whose partitions are considered
theta: Positive real number
Denotes Mutation rate
- Returns
RandomSymbol
Examples
>>> from sympy.stats import density, marginal_distribution, MultivariateEwens >>> from sympy import Symbol >>> a1 = Symbol('a1', positive=True) >>> a2 = Symbol('a2', positive=True) >>> ed = MultivariateEwens('E', 2, 1) >>> density(ed)(a1, a2) Piecewise((1/(2**a2*factorial(a1)*factorial(a2)), Eq(a1 + 2*a2, 2)), (0, True)) >>> marginal_distribution(ed, ed[0])(a1) Piecewise((1/factorial(a1), Eq(a1, 2)), (0, True))
References
- sympy.stats.MultivariateT(syms, mu, sigma, v)[source]¶
Creates a joint random variable with multivariate T-distribution.
- Parameters
syms: A symbol/str
For identifying the random variable.
mu: A list/matrix
Representing the location vector
sigma: The shape matrix for the distribution
- Returns
RandomSymbol
Examples
>>> from sympy.stats import density, MultivariateT >>> from sympy import Symbol
>>> x = Symbol("x") >>> X = MultivariateT("x", [1, 1], [[1, 0], [0, 1]], 2)
>>> density(X)(1, 2) 2/(9*pi)
- sympy.stats.NegativeMultinomial(syms, k0, *p)[source]¶
Creates a discrete random variable with Negative Multinomial Distribution.
The density of the said distribution can be found at [1].
- Parameters
k0: positive integer
Represents number of failures before the experiment is stopped
p: List of event probabilites
Must be in the range of \([0, 1]\)
- Returns
RandomSymbol
Examples
>>> from sympy.stats import density, NegativeMultinomial, marginal_distribution >>> from sympy import symbols >>> x1, x2, x3 = symbols('x1, x2, x3', nonnegative=True, integer=True) >>> p1, p2, p3 = symbols('p1, p2, p3', positive=True) >>> N = NegativeMultinomial('M', 3, p1, p2, p3) >>> N_c = NegativeMultinomial('M', 3, 0.1, 0.1, 0.1) >>> density(N)(x1, x2, x3) p1**x1*p2**x2*p3**x3*(-p1 - p2 - p3 + 1)**3*gamma(x1 + x2 + x3 + 3)/(2*factorial(x1)*factorial(x2)*factorial(x3)) >>> marginal_distribution(N_c, N_c[0])(1).evalf().round(2) 0.25
References
- sympy.stats.NormalGamma(sym, mu, lamda, alpha, beta)[source]¶
Creates a bivariate joint random variable with multivariate Normal gamma distribution.
- Parameters
sym: A symbol/str
For identifying the random variable.
mu: A real number
The mean of the normal distribution
lamda: A positive integer
Parameter of joint distribution
alpha: A positive integer
Parameter of joint distribution
beta: A positive integer
Parameter of joint distribution
- Returns
RandomSymbol
Examples
>>> from sympy.stats import density, NormalGamma >>> from sympy import symbols
>>> X = NormalGamma('x', 0, 1, 2, 3) >>> y, z = symbols('y z')
>>> density(X)(y, z) 9*sqrt(2)*z**(3/2)*exp(-3*z)*exp(-y**2*z/2)/(2*sqrt(pi))
References
Stochastic Processes¶
- class sympy.stats.DiscreteMarkovChain(sym, state_space=None, trans_probs=None)[source]¶
Represents a finite discrete time-homogeneous Markov chain.
This type of Markov Chain can be uniquely characterised by its (ordered) state space and its one-step transition probability matrix.
- Parameters
sym:
The name given to the Markov Chain
state_space:
Optional, by default, Range(n)
trans_probs:
Optional, by default, MatrixSymbol(‘_T’, n, n)
Examples
>>> from sympy.stats import DiscreteMarkovChain, TransitionMatrixOf, P, E >>> from sympy import Matrix, MatrixSymbol, Eq, symbols >>> T = Matrix([[0.5, 0.2, 0.3],[0.2, 0.5, 0.3],[0.2, 0.3, 0.5]]) >>> Y = DiscreteMarkovChain("Y", [0, 1, 2], T) >>> YS = DiscreteMarkovChain("Y")
>>> Y.state_space {0, 1, 2} >>> Y.transition_probabilities Matrix([ [0.5, 0.2, 0.3], [0.2, 0.5, 0.3], [0.2, 0.3, 0.5]]) >>> TS = MatrixSymbol('T', 3, 3) >>> P(Eq(YS[3], 2), Eq(YS[1], 1) & TransitionMatrixOf(YS, TS)) T[0, 2]*T[1, 0] + T[1, 1]*T[1, 2] + T[1, 2]*T[2, 2] >>> P(Eq(Y[3], 2), Eq(Y[1], 1)).round(2) 0.36
Probabilities will be calculated based on indexes rather than state names. For example, with the Sunny-Cloudy-Rainy model with string state names:
>>> from sympy.core.symbol import Str >>> Y = DiscreteMarkovChain("Y", [Str('Sunny'), Str('Cloudy'), Str('Rainy')], T) >>> P(Eq(Y[3], 2), Eq(Y[1], 1)).round(2) 0.36
This gives the same answer as the
[0, 1, 2]state space. Currently, there is no support for state names within probability and expectation statements. Here is a work-around usingStr:>>> P(Eq(Str('Rainy'), Y[3]), Eq(Y[1], Str('Cloudy'))).round(2) 0.36
Symbol state names can also be used:
>>> sunny, cloudy, rainy = symbols('Sunny, Cloudy, Rainy') >>> Y = DiscreteMarkovChain("Y", [sunny, cloudy, rainy], T) >>> P(Eq(Y[3], rainy), Eq(Y[1], cloudy)).round(2) 0.36
Expectations will be calculated as follows:
>>> E(Y[3], Eq(Y[1], cloudy)) 0.38*Cloudy + 0.36*Rainy + 0.26*Sunny
Probability of expressions with multiple RandomIndexedSymbols can also be calculated provided there is only 1 RandomIndexedSymbol in the given condition. It is always better to use Rational instead of floating point numbers for the probabilities in the transition matrix to avoid errors.
>>> from sympy import Gt, Le, Rational >>> T = Matrix([[Rational(5, 10), Rational(3, 10), Rational(2, 10)], [Rational(2, 10), Rational(7, 10), Rational(1, 10)], [Rational(3, 10), Rational(3, 10), Rational(4, 10)]]) >>> Y = DiscreteMarkovChain("Y", [0, 1, 2], T) >>> P(Eq(Y[3], Y[1]), Eq(Y[0], 0)).round(3) 0.409 >>> P(Gt(Y[3], Y[1]), Eq(Y[0], 0)).round(2) 0.36 >>> P(Le(Y[15], Y[10]), Eq(Y[8], 2)).round(7) 0.6963328
Symbolic probability queries are also supported
>>> a, b, c, d = symbols('a b c d') >>> T = Matrix([[Rational(1, 10), Rational(4, 10), Rational(5, 10)], [Rational(3, 10), Rational(4, 10), Rational(3, 10)], [Rational(7, 10), Rational(2, 10), Rational(1, 10)]]) >>> Y = DiscreteMarkovChain("Y", [0, 1, 2], T) >>> query = P(Eq(Y[a], b), Eq(Y[c], d)) >>> query.subs({a:10, b:2, c:5, d:1}).round(4) 0.3096 >>> P(Eq(Y[10], 2), Eq(Y[5], 1)).evalf().round(4) 0.3096 >>> query_gt = P(Gt(Y[a], b), Eq(Y[c], d)) >>> query_gt.subs({a:21, b:0, c:5, d:0}).evalf().round(5) 0.64705 >>> P(Gt(Y[21], 0), Eq(Y[5], 0)).round(5) 0.64705
There is limited support for arbitrarily sized states:
>>> n = symbols('n', nonnegative=True, integer=True) >>> T = MatrixSymbol('T', n, n) >>> Y = DiscreteMarkovChain("Y", trans_probs=T) >>> Y.state_space Range(0, n, 1) >>> query = P(Eq(Y[a], b), Eq(Y[c], d)) >>> query.subs({a:10, b:2, c:5, d:1}) (T**5)[1, 2]
References
- R917
https://en.wikipedia.org/wiki/Markov_chain#Discrete-time_Markov_chain
- R918
https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf
- absorbing_probabilities()[source]¶
Computes the absorbing probabilities, i.e., the ij-th entry of the matrix denotes the probability of Markov chain being absorbed in state j starting from state i.
- canonical_form() Tuple[List[sympy.core.basic.Basic], sympy.matrices.immutable.ImmutableDenseMatrix][source]¶
Reorders the one-step transition matrix so that recurrent states appear first and transient states appear last. Other representations include inserting transient states first and recurrent states last.
- Returns
states, P_new
statesis the list that describes the order of the new states in the matrix so that the ith element instatesis the state of the ith row of A.P_newis the new transition matrix in canonical form.
Examples
>>> from sympy.stats import DiscreteMarkovChain >>> from sympy import Matrix, S
You can convert your chain into canonical form:
>>> T = Matrix([[S(1)/2, S(1)/2, 0, 0, 0], ... [S(2)/5, S(1)/5, S(2)/5, 0, 0], ... [0, 0, 1, 0, 0], ... [0, 0, S(1)/2, S(1)/2, 0], ... [S(1)/2, 0, 0, 0, S(1)/2]]) >>> X = DiscreteMarkovChain('X', list(range(1, 6)), trans_probs=T) >>> states, new_matrix = X.canonical_form() >>> states [3, 1, 2, 4, 5]
>>> new_matrix Matrix([ [ 1, 0, 0, 0, 0], [ 0, 1/2, 1/2, 0, 0], [2/5, 2/5, 1/5, 0, 0], [1/2, 0, 0, 1/2, 0], [ 0, 1/2, 0, 0, 1/2]])
The new states are [3, 1, 2, 4, 5] and you can create a new chain with this and its canonical form will remain the same (since it is already in canonical form).
>>> X = DiscreteMarkovChain('X', states, new_matrix) >>> states, new_matrix = X.canonical_form() >>> states [3, 1, 2, 4, 5]
>>> new_matrix Matrix([ [ 1, 0, 0, 0, 0], [ 0, 1/2, 1/2, 0, 0], [2/5, 2/5, 1/5, 0, 0], [1/2, 0, 0, 1/2, 0], [ 0, 1/2, 0, 0, 1/2]])
This is not limited to absorbing chains:
>>> T = Matrix([[0, 5, 5, 0, 0], ... [0, 0, 0, 10, 0], ... [5, 0, 5, 0, 0], ... [0, 10, 0, 0, 0], ... [0, 3, 0, 3, 4]])/10 >>> X = DiscreteMarkovChain('X', trans_probs=T) >>> states, new_matrix = X.canonical_form() >>> states [1, 3, 0, 2, 4]
>>> new_matrix Matrix([ [ 0, 1, 0, 0, 0], [ 1, 0, 0, 0, 0], [ 1/2, 0, 0, 1/2, 0], [ 0, 0, 1/2, 1/2, 0], [3/10, 3/10, 0, 0, 2/5]])
See also
sympy.stats.DiscreteMarkovChain.communication_classes,sympy.stats.DiscreteMarkovChain.decomposeReferences
- communication_classes() List[Tuple[List[sympy.core.basic.Basic], sympy.logic.boolalg.Boolean, sympy.core.numbers.Integer]][source]¶
Returns the list of communication classes that partition the states of the markov chain.
A communication class is defined to be a set of states such that every state in that set is reachable from every other state in that set. Due to its properties this forms a class in the mathematical sense. Communication classes are also known as recurrence classes.
- Returns
classes
The
classesare a list of tuples. Each tuple represents a single communication class with its properties. The first element in the tuple is the list of states in the class, the second element is whether the class is recurrent and the third element is the period of the communication class.
Examples
>>> from sympy.stats import DiscreteMarkovChain >>> from sympy import Matrix >>> T = Matrix([[0, 1, 0], ... [1, 0, 0], ... [1, 0, 0]]) >>> X = DiscreteMarkovChain('X', [1, 2, 3], T) >>> classes = X.communication_classes() >>> for states, is_recurrent, period in classes: ... states, is_recurrent, period ([1, 2], True, 2) ([3], False, 1)
From this we can see that states
1and2communicate, are recurrent and have a period of 2. We can also see state3is transient with a period of 1.Notes
The algorithm used is of order
O(n**2)wherenis the number of states in the markov chain. It uses Tarjan’s algorithm to find the classes themselves and then it uses a breadth-first search algorithm to find each class’s periodicity. Most of the algorithm’s components approachO(n)as the matrix becomes more and more sparse.References
- decompose() Tuple[List[sympy.core.basic.Basic], sympy.matrices.immutable.ImmutableDenseMatrix, sympy.matrices.immutable.ImmutableDenseMatrix, sympy.matrices.immutable.ImmutableDenseMatrix][source]¶
Decomposes the transition matrix into submatrices with special properties.
The transition matrix can be decomposed into 4 submatrices: - A - the submatrix from recurrent states to recurrent states. - B - the submatrix from transient to recurrent states. - C - the submatrix from transient to transient states. - O - the submatrix of zeros for recurrent to transient states.
- Returns
states, A, B, C
states- a list of state names with the first being the recurrent states and the last being the transient states in the order of the row names of A and then the row names of C.A- the submatrix from recurrent states to recurrent states.B- the submatrix from transient to recurrent states.C- the submatrix from transient to transient states.
Examples
>>> from sympy.stats import DiscreteMarkovChain >>> from sympy import Matrix, S
One can decompose this chain for example:
>>> T = Matrix([[S(1)/2, S(1)/2, 0, 0, 0], ... [S(2)/5, S(1)/5, S(2)/5, 0, 0], ... [0, 0, 1, 0, 0], ... [0, 0, S(1)/2, S(1)/2, 0], ... [S(1)/2, 0, 0, 0, S(1)/2]]) >>> X = DiscreteMarkovChain('X', trans_probs=T) >>> states, A, B, C = X.decompose() >>> states [2, 0, 1, 3, 4]
>>> A # recurrent to recurrent Matrix([[1]])
>>> B # transient to recurrent Matrix([ [ 0], [2/5], [1/2], [ 0]])
>>> C # transient to transient Matrix([ [1/2, 1/2, 0, 0], [2/5, 1/5, 0, 0], [ 0, 0, 1/2, 0], [1/2, 0, 0, 1/2]])
This means that state 2 is the only absorbing state (since A is a 1x1 matrix). B is a 4x1 matrix since the 4 remaining transient states all merge into reccurent state 2. And C is the 4x4 matrix that shows how the transient states 0, 1, 3, 4 all interact.
See also
sympy.stats.DiscreteMarkovChain.communication_classes,sympy.stats.DiscreteMarkovChain.canonical_formReferences
- fundamental_matrix()[source]¶
Each entry fundamental matrix can be interpreted as the expected number of times the chains is in state j if it started in state i.
References
- property limiting_distribution¶
The fixed row vector is the limiting distribution of a discrete Markov chain.
- stationary_distribution(condition_set=False) Union[sympy.matrices.immutable.ImmutableDenseMatrix, sympy.sets.conditionset.ConditionSet, sympy.core.function.Lambda][source]¶
The stationary distribution is any row vector, p, that solves p = pP, is row stochastic and each element in p must be nonnegative. That means in matrix form: \((P-I)^T p^T = 0\) and \((1, \dots, 1) p = 1\) where
Pis the one-step transition matrix.All time-homogeneous Markov Chains with a finite state space have at least one stationary distribution. In addition, if a finite time-homogeneous Markov Chain is irreducible, the stationary distribution is unique.
- Parameters
condition_set : bool
If the chain has a symbolic size or transition matrix, it will return a
LambdaifFalseand return aConditionSetifTrue.
Examples
>>> from sympy.stats import DiscreteMarkovChain >>> from sympy import Matrix, S
An irreducible Markov Chain
>>> T = Matrix([[S(1)/2, S(1)/2, 0], ... [S(4)/5, S(1)/5, 0], ... [1, 0, 0]]) >>> X = DiscreteMarkovChain('X', trans_probs=T) >>> X.stationary_distribution() Matrix([[8/13, 5/13, 0]])
A reducible Markov Chain
>>> T = Matrix([[S(1)/2, S(1)/2, 0], ... [S(4)/5, S(1)/5, 0], ... [0, 0, 1]]) >>> X = DiscreteMarkovChain('X', trans_probs=T) >>> X.stationary_distribution() Matrix([[8/13 - 8*tau0/13, 5/13 - 5*tau0/13, tau0]])
>>> Y = DiscreteMarkovChain('Y') >>> Y.stationary_distribution() Lambda((wm, _T), Eq(wm*_T, wm))
>>> Y.stationary_distribution(condition_set=True) ConditionSet(wm, Eq(wm*_T, wm))
References
- property transition_probabilities¶
Transition probabilities of discrete Markov chain, either an instance of Matrix or MatrixSymbol.
- class sympy.stats.ContinuousMarkovChain(sym, state_space=None, gen_mat=None)[source]¶
Represents continuous time Markov chain.
- Parameters
sym: Symbol/str
state_space: Set
Optional, by default, S.Reals
gen_mat: Matrix/ImmutableMatrix/MatrixSymbol
Optional, by default, None
Examples
>>> from sympy.stats import ContinuousMarkovChain, P >>> from sympy import Matrix, S, Eq, Gt >>> G = Matrix([[-S(1), S(1)], [S(1), -S(1)]]) >>> C = ContinuousMarkovChain('C', state_space=[0, 1], gen_mat=G) >>> C.limiting_distribution() Matrix([[1/2, 1/2]]) >>> C.state_space {0, 1} >>> C.generator_matrix Matrix([ [-1, 1], [ 1, -1]])
Probability queries are supported
>>> P(Eq(C(1.96), 0), Eq(C(0.78), 1)).round(5) 0.45279 >>> P(Gt(C(1.7), 0), Eq(C(0.82), 1)).round(5) 0.58602
Probability of expressions with multiple RandomIndexedSymbols can also be calculated provided there is only 1 RandomIndexedSymbol in the given condition. It is always better to use Rational instead of floating point numbers for the probabilities in the generator matrix to avoid errors.
>>> from sympy import Gt, Le, Rational >>> G = Matrix([[-S(1), Rational(1, 10), Rational(9, 10)], [Rational(2, 5), -S(1), Rational(3, 5)], [Rational(1, 2), Rational(1, 2), -S(1)]]) >>> C = ContinuousMarkovChain('C', state_space=[0, 1, 2], gen_mat=G) >>> P(Eq(C(3.92), C(1.75)), Eq(C(0.46), 0)).round(5) 0.37933 >>> P(Gt(C(3.92), C(1.75)), Eq(C(0.46), 0)).round(5) 0.34211 >>> P(Le(C(1.57), C(3.14)), Eq(C(1.22), 1)).round(4) 0.7143
Symbolic probability queries are also supported
>>> from sympy import symbols >>> a,b,c,d = symbols('a b c d') >>> G = Matrix([[-S(1), Rational(1, 10), Rational(9, 10)], [Rational(2, 5), -S(1), Rational(3, 5)], [Rational(1, 2), Rational(1, 2), -S(1)]]) >>> C = ContinuousMarkovChain('C', state_space=[0, 1, 2], gen_mat=G) >>> query = P(Eq(C(a), b), Eq(C(c), d)) >>> query.subs({a:3.65, b:2, c:1.78, d:1}).evalf().round(10) 0.4002723175 >>> P(Eq(C(3.65), 2), Eq(C(1.78), 1)).round(10) 0.4002723175 >>> query_gt = P(Gt(C(a), b), Eq(C(c), d)) >>> query_gt.subs({a:43.2, b:0, c:3.29, d:2}).evalf().round(10) 0.6832579186 >>> P(Gt(C(43.2), 0), Eq(C(3.29), 2)).round(10) 0.6832579186
References
- class sympy.stats.BernoulliProcess(sym, p, success=1, failure=0)[source]¶
The Bernoulli process consists of repeated independent Bernoulli process trials with the same parameter \(p\). It’s assumed that the probability \(p\) applies to every trial and that the outcomes of each trial are independent of all the rest. Therefore Bernoulli Processs is Discrete State and Discrete Time Stochastic Process.
- Parameters
sym: Symbol/str
success: Integer/str
The event which is considered to be success, by default is 1.
failure: Integer/str
The event which is considered to be failure, by default is 0.
p: Real Number between 0 and 1
Represents the probability of getting success.
Examples
>>> from sympy.stats import BernoulliProcess, P, E >>> from sympy import Eq, Gt >>> B = BernoulliProcess("B", p=0.7, success=1, failure=0) >>> B.state_space {0, 1} >>> (B.p).round(2) 0.70 >>> B.success 1 >>> B.failure 0 >>> X = B[1] + B[2] + B[3] >>> P(Eq(X, 0)).round(2) 0.03 >>> P(Eq(X, 2)).round(2) 0.44 >>> P(Eq(X, 4)).round(2) 0 >>> P(Gt(X, 1)).round(2) 0.78 >>> P(Eq(B[1], 0) & Eq(B[2], 1) & Eq(B[3], 0) & Eq(B[4], 1)).round(2) 0.04 >>> B.joint_distribution(B[1], B[2]) JointDistributionHandmade(Lambda((B[1], B[2]), Piecewise((0.7, Eq(B[1], 1)), (0.3, Eq(B[1], 0)), (0, True))*Piecewise((0.7, Eq(B[2], 1)), (0.3, Eq(B[2], 0)), (0, True)))) >>> E(2*B[1] + B[2]).round(2) 2.10 >>> P(B[1] < 1).round(2) 0.30
References
- expectation(expr, condition=None, evaluate=True, **kwargs)[source]¶
Computes expectation.
- Parameters
expr: RandomIndexedSymbol, Relational, Logic
Condition for which expectation has to be computed. Must contain a RandomIndexedSymbol of the process.
condition: Relational, Logic
The given conditions under which computations should be done.
- Returns
Expectation of the RandomIndexedSymbol.
- probability(condition, given_condition=None, evaluate=True, **kwargs)[source]¶
Computes probability.
- Parameters
condition: Relational
Condition for which probability has to be computed. Must contain a RandomIndexedSymbol of the process.
given_condition: Relational/And
The given conditions under which computations should be done.
- Returns
Probability of the condition.
- class sympy.stats.PoissonProcess(sym, lamda)[source]¶
The Poisson process is a counting process. It is usually used in scenarios where we are counting the occurrences of certain events that appear to happen at a certain rate, but completely at random.
- Parameters
sym: Symbol/str
lamda: Positive number
Rate of the process,
lamda > 0
Examples
>>> from sympy.stats import PoissonProcess, P, E >>> from sympy import symbols, Eq, Ne, Contains, Interval >>> X = PoissonProcess("X", lamda=3) >>> X.state_space Naturals0 >>> X.lamda 3 >>> t1, t2 = symbols('t1 t2', positive=True) >>> P(X(t1) < 4) (9*t1**3/2 + 9*t1**2/2 + 3*t1 + 1)*exp(-3*t1) >>> P(Eq(X(t1), 2) | Ne(X(t1), 4), Contains(t1, Interval.Ropen(2, 4))) 1 - 36*exp(-6) >>> P(Eq(X(t1), 2) & Eq(X(t2), 3), Contains(t1, Interval.Lopen(0, 2)) ... & Contains(t2, Interval.Lopen(2, 4))) 648*exp(-12) >>> E(X(t1)) 3*t1 >>> E(X(t1)**2 + 2*X(t2), Contains(t1, Interval.Lopen(0, 1)) ... & Contains(t2, Interval.Lopen(1, 2))) 18 >>> P(X(3) < 1, Eq(X(1), 0)) exp(-6) >>> P(Eq(X(4), 3), Eq(X(2), 3)) exp(-6) >>> P(X(2) <= 3, X(1) > 1) 5*exp(-3)
Merging two Poisson Processes
>>> Y = PoissonProcess("Y", lamda=4) >>> Z = X + Y >>> Z.lamda 7
Splitting a Poisson Process into two independent Poisson Processes
>>> N, M = Z.split(l1=2, l2=5) >>> N.lamda, M.lamda (2, 5)
References
- class sympy.stats.WienerProcess(sym)[source]¶
The Wiener process is a real valued continuous-time stochastic process. In physics it is used to study Brownian motion and it is often also called Brownian motion due to its historical connection with physical process of the same name originally observed by Scottish botanist Robert Brown.
- Parameters
sym: Symbol/str
Examples
>>> from sympy.stats import WienerProcess, P, E >>> from sympy import symbols, Contains, Interval >>> X = WienerProcess("X") >>> X.state_space Reals >>> t1, t2 = symbols('t1 t2', positive=True) >>> P(X(t1) < 7).simplify() erf(7*sqrt(2)/(2*sqrt(t1)))/2 + 1/2 >>> P((X(t1) > 2) | (X(t1) < 4), Contains(t1, Interval.Ropen(2, 4))).simplify() -erf(1)/2 + erf(2)/2 + 1 >>> E(X(t1)) 0 >>> E(X(t1) + 2*X(t2), Contains(t1, Interval.Lopen(0, 1)) ... & Contains(t2, Interval.Lopen(1, 2))) 0
References
- class sympy.stats.GammaProcess(sym, lamda, gamma)[source]¶
A Gamma process is a random process with independent gamma distributed increments. It is a pure-jump increasing Levy process.
- Parameters
sym: Symbol/str
lamda: Positive number
Jump size of the process,
lamda > 0gamma: Positive number
Rate of jump arrivals,
gamma > 0
Examples
>>> from sympy.stats import GammaProcess, E, P, variance >>> from sympy import symbols, Contains, Interval, Not >>> t, d, x, l, g = symbols('t d x l g', positive=True) >>> X = GammaProcess("X", l, g) >>> E(X(t)) g*t/l >>> variance(X(t)).simplify() g*t/l**2 >>> X = GammaProcess('X', 1, 2) >>> P(X(t) < 1).simplify() lowergamma(2*t, 1)/gamma(2*t) >>> P(Not((X(t) < 5) & (X(d) > 3)), Contains(t, Interval.Ropen(2, 4)) & ... Contains(d, Interval.Lopen(7, 8))).simplify() -4*exp(-3) + 472*exp(-8)/3 + 1 >>> E(X(2) + x*E(X(5))) 10*x + 4
References
Matrix Distributions¶
- sympy.stats.MatrixGamma(symbol, alpha, beta, scale_matrix)[source]¶
Creates a random variable with Matrix Gamma Distribution.
The density of the said distribution can be found at [1].
- Parameters
alpha: Positive Real number
Shape Parameter
beta: Positive Real number
Scale Parameter
scale_matrix: Positive definite real square matrix
Scale Matrix
- Returns
RandomSymbol
Examples
>>> from sympy.stats import density, MatrixGamma >>> from sympy import MatrixSymbol, symbols >>> a, b = symbols('a b', positive=True) >>> M = MatrixGamma('M', a, b, [[2, 1], [1, 2]]) >>> X = MatrixSymbol('X', 2, 2) >>> density(M)(X).doit() exp(Trace(Matrix([ [-2/3, 1/3], [ 1/3, -2/3]])*X)/b)*Determinant(X)**(a - 3/2)/(3**a*sqrt(pi)*b**(2*a)*gamma(a)*gamma(a - 1/2)) >>> density(M)([[1, 0], [0, 1]]).doit() exp(-4/(3*b))/(3**a*sqrt(pi)*b**(2*a)*gamma(a)*gamma(a - 1/2))
References
- sympy.stats.Wishart(symbol, n, scale_matrix)[source]¶
Creates a random variable with Wishart Distribution.
The density of the said distribution can be found at [1].
- Parameters
n: Positive Real number
Represents degrees of freedom
scale_matrix: Positive definite real square matrix
Scale Matrix
- Returns
RandomSymbol
Examples
>>> from sympy.stats import density, Wishart >>> from sympy import MatrixSymbol, symbols >>> n = symbols('n', positive=True) >>> W = Wishart('W', n, [[2, 1], [1, 2]]) >>> X = MatrixSymbol('X', 2, 2) >>> density(W)(X).doit() exp(Trace(Matrix([ [-1/3, 1/6], [ 1/6, -1/3]])*X))*Determinant(X)**(n/2 - 3/2)/(2**n*3**(n/2)*sqrt(pi)*gamma(n/2)*gamma(n/2 - 1/2)) >>> density(W)([[1, 0], [0, 1]]).doit() exp(-2/3)/(2**n*3**(n/2)*sqrt(pi)*gamma(n/2)*gamma(n/2 - 1/2))
References
- sympy.stats.MatrixNormal(symbol, location_matrix, scale_matrix_1, scale_matrix_2)[source]¶
Creates a random variable with Matrix Normal Distribution.
The density of the said distribution can be found at [1].
- Parameters
location_matrix: Real ``n x p`` matrix
Represents degrees of freedom
scale_matrix_1: Positive definite matrix
Scale Matrix of shape
n x nscale_matrix_2: Positive definite matrix
Scale Matrix of shape
p x p- Returns
RandomSymbol
Examples
>>> from sympy import MatrixSymbol >>> from sympy.stats import density, MatrixNormal >>> M = MatrixNormal('M', [[1, 2]], [1], [[1, 0], [0, 1]]) >>> X = MatrixSymbol('X', 1, 2) >>> density(M)(X).doit() 2*exp(-Trace((Matrix([ [-1], [-2]]) + X.T)*(Matrix([[-1, -2]]) + X))/2)/pi >>> density(M)([[3, 4]]).doit() 2*exp(-4)/pi
References
Compound Distribution¶
- class sympy.stats.compound_rv.CompoundDistribution(dist)[source]¶
Class for Compound Distributions.
- Parameters
dist : Distribution
Distribution must contain a random parameter
Examples
>>> from sympy.stats.compound_rv import CompoundDistribution >>> from sympy.stats.crv_types import NormalDistribution >>> from sympy.stats import Normal >>> from sympy.abc import x >>> X = Normal('X', 2, 4) >>> N = NormalDistribution(X, 4) >>> C = CompoundDistribution(N) >>> C.set Interval(-oo, oo) >>> C.pdf(x, evaluate=True).simplify() exp(-x**2/64 + x/16 - 1/16)/(8*sqrt(pi))
References
Interface¶
- sympy.stats.P(condition, given_condition=None, numsamples=None, evaluate=True, **kwargs)[source]¶
Probability that a condition is true, optionally given a second condition.
- Parameters
condition : Combination of Relationals containing RandomSymbols
The condition of which you want to compute the probability
given_condition : Combination of Relationals containing RandomSymbols
A conditional expression. P(X > 1, X > 0) is expectation of X > 1 given X > 0
numsamples : int
Enables sampling and approximates the probability with this many samples
evaluate : Bool (defaults to True)
In case of continuous systems return unevaluated integral
Examples
>>> from sympy.stats import P, Die >>> from sympy import Eq >>> X, Y = Die('X', 6), Die('Y', 6) >>> P(X > 3) 1/2 >>> P(Eq(X, 5), X > 2) # Probability that X == 5 given that X > 2 1/4 >>> P(X > Y) 5/12
- class sympy.stats.Probability(prob, condition=None, **kwargs)[source]¶
Symbolic expression for the probability.
Examples
>>> from sympy.stats import Probability, Normal >>> from sympy import Integral >>> X = Normal("X", 0, 1) >>> prob = Probability(X > 1) >>> prob Probability(X > 1)
Integral representation:
>>> prob.rewrite(Integral) Integral(sqrt(2)*exp(-_z**2/2)/(2*sqrt(pi)), (_z, 1, oo))
Evaluation of the integral:
>>> prob.evaluate_integral() sqrt(2)*(-sqrt(2)*sqrt(pi)*erf(sqrt(2)/2) + sqrt(2)*sqrt(pi))/(4*sqrt(pi))
- sympy.stats.E(expr, condition=None, numsamples=None, evaluate=True, **kwargs)[source]¶
Returns the expected value of a random expression.
- Parameters
expr : Expr containing RandomSymbols
The expression of which you want to compute the expectation value
given : Expr containing RandomSymbols
A conditional expression. E(X, X>0) is expectation of X given X > 0
numsamples : int
Enables sampling and approximates the expectation with this many samples
evalf : Bool (defaults to True)
If sampling return a number rather than a complex expression
evaluate : Bool (defaults to True)
In case of continuous systems return unevaluated integral
Examples
>>> from sympy.stats import E, Die >>> X = Die('X', 6) >>> E(X) 7/2 >>> E(2*X + 1) 8
>>> E(X, X > 3) # Expectation of X given that it is above 3 5
- class sympy.stats.Expectation(expr, condition=None, **kwargs)[source]¶
Symbolic expression for the expectation.
Examples
>>> from sympy.stats import Expectation, Normal, Probability, Poisson >>> from sympy import symbols, Integral, Sum >>> mu = symbols("mu") >>> sigma = symbols("sigma", positive=True) >>> X = Normal("X", mu, sigma) >>> Expectation(X) Expectation(X) >>> Expectation(X).evaluate_integral().simplify() mu
To get the integral expression of the expectation:
>>> Expectation(X).rewrite(Integral) Integral(sqrt(2)*X*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo))
The same integral expression, in more abstract terms:
>>> Expectation(X).rewrite(Probability) Integral(x*Probability(Eq(X, x)), (x, -oo, oo))
To get the Summation expression of the expectation for discrete random variables:
>>> lamda = symbols('lamda', positive=True) >>> Z = Poisson('Z', lamda) >>> Expectation(Z).rewrite(Sum) Sum(Z*lamda**Z*exp(-lamda)/factorial(Z), (Z, 0, oo))
This class is aware of some properties of the expectation:
>>> from sympy.abc import a >>> Expectation(a*X) Expectation(a*X) >>> Y = Normal("Y", 1, 2) >>> Expectation(X + Y) Expectation(X + Y)
To expand the
Expectationinto its expression, useexpand():>>> Expectation(X + Y).expand() Expectation(X) + Expectation(Y) >>> Expectation(a*X + Y).expand() a*Expectation(X) + Expectation(Y) >>> Expectation(a*X + Y) Expectation(a*X + Y) >>> Expectation((X + Y)*(X - Y)).expand() Expectation(X**2) - Expectation(Y**2)
To evaluate the
Expectation, usedoit():>>> Expectation(X + Y).doit() mu + 1 >>> Expectation(X + Expectation(Y + Expectation(2*X))).doit() 3*mu + 1
To prevent evaluating nested
Expectation, usedoit(deep=False)>>> Expectation(X + Expectation(Y)).doit(deep=False) mu + Expectation(Expectation(Y)) >>> Expectation(X + Expectation(Y + Expectation(2*X))).doit(deep=False) mu + Expectation(Expectation(Y + Expectation(2*X)))
- sympy.stats.density(expr, condition=None, evaluate=True, numsamples=None, **kwargs)[source]¶
Probability density of a random expression, optionally given a second condition.
- Parameters
expr : Expr containing RandomSymbols
The expression of which you want to compute the density value
condition : Relational containing RandomSymbols
A conditional expression. density(X > 1, X > 0) is density of X > 1 given X > 0
numsamples : int
Enables sampling and approximates the density with this many samples
Explanation
This density will take on different forms for different types of probability spaces. Discrete variables produce Dicts. Continuous variables produce Lambdas.
Examples
>>> from sympy.stats import density, Die, Normal >>> from sympy import Symbol
>>> x = Symbol('x') >>> D = Die('D', 6) >>> X = Normal(x, 0, 1)
>>> density(D).dict {1: 1/6, 2: 1/6, 3: 1/6, 4: 1/6, 5: 1/6, 6: 1/6} >>> density(2*D).dict {2: 1/6, 4: 1/6, 6: 1/6, 8: 1/6, 10: 1/6, 12: 1/6} >>> density(X)(x) sqrt(2)*exp(-x**2/2)/(2*sqrt(pi))
- sympy.stats.entropy(expr, condition=None, **kwargs)[source]¶
Calculuates entropy of a probability distribution.
- Parameters
expression : the random expression whose entropy is to be calculated
condition : optional, to specify conditions on random expression
b: base of the logarithm, optional
By default, it is taken as Euler’s number
- Returns
result : Entropy of the expression, a constant
Examples
>>> from sympy.stats import Normal, Die, entropy >>> X = Normal('X', 0, 1) >>> entropy(X) log(2)/2 + 1/2 + log(pi)/2
>>> D = Die('D', 4) >>> entropy(D) log(4)
References
- sympy.stats.given(expr, condition=None, **kwargs)[source]¶
Conditional Random Expression.
Explanation
From a random expression and a condition on that expression creates a new probability space from the condition and returns the same expression on that conditional probability space.
Examples
>>> from sympy.stats import given, density, Die >>> X = Die('X', 6) >>> Y = given(X, X > 3) >>> density(Y).dict {4: 1/3, 5: 1/3, 6: 1/3}
Following convention, if the condition is a random symbol then that symbol is considered fixed.
>>> from sympy.stats import Normal >>> from sympy import pprint >>> from sympy.abc import z
>>> X = Normal('X', 0, 1) >>> Y = Normal('Y', 0, 1) >>> pprint(density(X + Y, Y)(z), use_unicode=False) 2 -(-Y + z) ----------- ___ 2 \/ 2 *e ------------------ ____ 2*\/ pi
- sympy.stats.where(condition, given_condition=None, **kwargs)[source]¶
Returns the domain where a condition is True.
Examples
>>> from sympy.stats import where, Die, Normal >>> from sympy import And
>>> D1, D2 = Die('a', 6), Die('b', 6) >>> a, b = D1.symbol, D2.symbol >>> X = Normal('x', 0, 1)
>>> where(X**2<1) Domain: (-1 < x) & (x < 1)
>>> where(X**2<1).set Interval.open(-1, 1)
>>> where(And(D1<=D2, D2<3)) Domain: (Eq(a, 1) & Eq(b, 1)) | (Eq(a, 1) & Eq(b, 2)) | (Eq(a, 2) & Eq(b, 2))
- sympy.stats.variance(X, condition=None, **kwargs)[source]¶
Variance of a random expression.
\[variance(X) = E((X-E(X))^{2})\]Examples
>>> from sympy.stats import Die, Bernoulli, variance >>> from sympy import simplify, Symbol
>>> X = Die('X', 6) >>> p = Symbol('p') >>> B = Bernoulli('B', p, 1, 0)
>>> variance(2*X) 35/3
>>> simplify(variance(B)) p*(1 - p)
- class sympy.stats.Variance(arg, condition=None, **kwargs)[source]¶
Symbolic expression for the variance.
Examples
>>> from sympy import symbols, Integral >>> from sympy.stats import Normal, Expectation, Variance, Probability >>> mu = symbols("mu", positive=True) >>> sigma = symbols("sigma", positive=True) >>> X = Normal("X", mu, sigma) >>> Variance(X) Variance(X) >>> Variance(X).evaluate_integral() sigma**2
Integral representation of the underlying calculations:
>>> Variance(X).rewrite(Integral) Integral(sqrt(2)*(X - Integral(sqrt(2)*X*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo)))**2*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo))
Integral representation, without expanding the PDF:
>>> Variance(X).rewrite(Probability) -Integral(x*Probability(Eq(X, x)), (x, -oo, oo))**2 + Integral(x**2*Probability(Eq(X, x)), (x, -oo, oo))
Rewrite the variance in terms of the expectation
>>> Variance(X).rewrite(Expectation) -Expectation(X)**2 + Expectation(X**2)
Some transformations based on the properties of the variance may happen:
>>> from sympy.abc import a >>> Y = Normal("Y", 0, 1) >>> Variance(a*X) Variance(a*X)
To expand the variance in its expression, use
expand():>>> Variance(a*X).expand() a**2*Variance(X) >>> Variance(X + Y) Variance(X + Y) >>> Variance(X + Y).expand() 2*Covariance(X, Y) + Variance(X) + Variance(Y)
- sympy.stats.covariance(X, Y, condition=None, **kwargs)[source]¶
Covariance of two random expressions.
Explanation
The expectation that the two variables will rise and fall together
\[covariance(X,Y) = E((X-E(X)) (Y-E(Y)))\]Examples
>>> from sympy.stats import Exponential, covariance >>> from sympy import Symbol
>>> rate = Symbol('lambda', positive=True, real=True) >>> X = Exponential('X', rate) >>> Y = Exponential('Y', rate)
>>> covariance(X, X) lambda**(-2) >>> covariance(X, Y) 0 >>> covariance(X, Y + rate*X) 1/lambda
- class sympy.stats.Covariance(arg1, arg2, condition=None, **kwargs)[source]¶
Symbolic expression for the covariance.
Examples
>>> from sympy.stats import Covariance >>> from sympy.stats import Normal >>> X = Normal("X", 3, 2) >>> Y = Normal("Y", 0, 1) >>> Z = Normal("Z", 0, 1) >>> W = Normal("W", 0, 1) >>> cexpr = Covariance(X, Y) >>> cexpr Covariance(X, Y)
Evaluate the covariance, \(X\) and \(Y\) are independent, therefore zero is the result:
>>> cexpr.evaluate_integral() 0
Rewrite the covariance expression in terms of expectations:
>>> from sympy.stats import Expectation >>> cexpr.rewrite(Expectation) Expectation(X*Y) - Expectation(X)*Expectation(Y)
In order to expand the argument, use
expand():>>> from sympy.abc import a, b, c, d >>> Covariance(a*X + b*Y, c*Z + d*W) Covariance(a*X + b*Y, c*Z + d*W) >>> Covariance(a*X + b*Y, c*Z + d*W).expand() a*c*Covariance(X, Z) + a*d*Covariance(W, X) + b*c*Covariance(Y, Z) + b*d*Covariance(W, Y)
This class is aware of some properties of the covariance:
>>> Covariance(X, X).expand() Variance(X) >>> Covariance(a*X, b*Y).expand() a*b*Covariance(X, Y)
- sympy.stats.coskewness(X, Y, Z, condition=None, **kwargs)[source]¶
Calculates the co-skewness of three random variables.
- Parameters
X : RandomSymbol
Random Variable used to calculate coskewness
Y : RandomSymbol
Random Variable used to calculate coskewness
Z : RandomSymbol
Random Variable used to calculate coskewness
condition : Expr containing RandomSymbols
A conditional expression
- Returns
coskewness : The coskewness of the three random variables
Explanation
Mathematically Coskewness is defined as
\[coskewness(X,Y,Z)=\frac{E[(X-E[X]) * (Y-E[Y]) * (Z-E[Z])]} {\sigma_{X}\sigma_{Y}\sigma_{Z}}\]Examples
>>> from sympy.stats import coskewness, Exponential, skewness >>> from sympy import symbols >>> p = symbols('p', positive=True) >>> X = Exponential('X', p) >>> Y = Exponential('Y', 2*p) >>> coskewness(X, Y, Y) 0 >>> coskewness(X, Y + X, Y + 2*X) 16*sqrt(85)/85 >>> coskewness(X + 2*Y, Y + X, Y + 2*X, X > 3) 9*sqrt(170)/85 >>> coskewness(Y, Y, Y) == skewness(Y) True >>> coskewness(X, Y + p*X, Y + 2*p*X) 4/(sqrt(1 + 1/(4*p**2))*sqrt(4 + 1/(4*p**2)))
References
- sympy.stats.median(X, evaluate=True, **kwargs)[source]¶
Calculuates the median of the probability distribution.
- Parameters
X: The random expression whose median is to be calculated.
- Returns
The FiniteSet or an Interval which contains the median of the
random expression.
Explanation
Mathematically, median of Probability distribution is defined as all those values of \(m\) for which the following condition is satisfied
\[P(X\leq m) \geq \frac{1}{2} \text{ and} \text{ } P(X\geq m)\geq \frac{1}{2}\]Examples
>>> from sympy.stats import Normal, Die, median >>> N = Normal('N', 3, 1) >>> median(N) {3} >>> D = Die('D') >>> median(D) {3, 4}
References
- sympy.stats.std(X, condition=None, **kwargs)[source]¶
Standard Deviation of a random expression
\[std(X) = \sqrt(E((X-E(X))^{2}))\]Examples
>>> from sympy.stats import Bernoulli, std >>> from sympy import Symbol, simplify
>>> p = Symbol('p') >>> B = Bernoulli('B', p, 1, 0)
>>> simplify(std(B)) sqrt(p*(1 - p))
- sympy.stats.quantile(expr, evaluate=True, **kwargs)[source]¶
Return the \(p^{th}\) order quantile of a probability distribution.
Explanation
Quantile is defined as the value at which the probability of the random variable is less than or equal to the given probability.
- ..math::
Q(p) = inf{x in (-infty, infty) such that p <= F(x)}
Examples
>>> from sympy.stats import quantile, Die, Exponential >>> from sympy import Symbol, pprint >>> p = Symbol("p")
>>> l = Symbol("lambda", positive=True) >>> X = Exponential("x", l) >>> quantile(X)(p) -log(1 - p)/lambda
>>> D = Die("d", 6) >>> pprint(quantile(D)(p), use_unicode=False) /nan for Or(p > 1, p < 0) | | 1 for p <= 1/6 | | 2 for p <= 1/3 | < 3 for p <= 1/2 | | 4 for p <= 2/3 | | 5 for p <= 5/6 | \ 6 for p <= 1
- sympy.stats.sample(expr, condition=None, size=(), library='scipy', numsamples=1, seed=None, **kwargs)[source]¶
A realization of the random expression.
- Parameters
expr : Expression of random variables
Expression from which sample is extracted
condition : Expr containing RandomSymbols
A conditional expression
size : int, tuple
Represents size of each sample in numsamples
library : str
‘scipy’ : Sample using scipy
‘numpy’ : Sample using numpy
‘pymc3’ : Sample using PyMC3
Choose any of the available options to sample from as string, by default is ‘scipy’
numsamples : int
Number of samples, each with size as
size.Deprecated since version 1.9.
The
numsamplesparameter is deprecated and is only provided for compatibility with v1.8. Use a list comprehension or an additional dimension insizeinstead. See sympy.stats.sample(numsamples=n) for details.seed :
An object to be used as seed by the given external library for sampling \(expr\). Following is the list of possible types of object for the supported libraries,
‘scipy’: int, numpy.random.RandomState, numpy.random.Generator
‘numpy’: int, numpy.random.RandomState, numpy.random.Generator
‘pymc3’: int
Optional, by default None, in which case seed settings related to the given library will be used. No modifications to environment’s global seed settings are done by this argument.
- Returns
sample: float/list/numpy.ndarray
one sample or a collection of samples of the random expression.
sample(X) returns float/numpy.float64/numpy.int64 object.
sample(X, size=int/tuple) returns numpy.ndarray object.
Examples
>>> from sympy.stats import Die, sample, Normal, Geometric >>> X, Y, Z = Die('X', 6), Die('Y', 6), Die('Z', 6) # Finite Random Variable >>> die_roll = sample(X + Y + Z) >>> die_roll 3 >>> N = Normal('N', 3, 4) # Continuous Random Variable >>> samp = sample(N) >>> samp in N.pspace.domain.set True >>> samp = sample(N, N>0) >>> samp > 0 True >>> samp_list = sample(N, size=4) >>> [sam in N.pspace.domain.set for sam in samp_list] [True, True, True, True] >>> sample(N, size = (2,3)) array([[5.42519758, 6.40207856, 4.94991743], [1.85819627, 6.83403519, 1.9412172 ]]) >>> G = Geometric('G', 0.5) # Discrete Random Variable >>> samp_list = sample(G, size=3) >>> samp_list [1, 3, 2] >>> [sam in G.pspace.domain.set for sam in samp_list] [True, True, True] >>> MN = Normal("MN", [3, 4], [[2, 1], [1, 2]]) # Joint Random Variable >>> samp_list = sample(MN, size=4) >>> samp_list [array([2.85768055, 3.38954165]), array([4.11163337, 4.3176591 ]), array([0.79115232, 1.63232916]), array([4.01747268, 3.96716083])] >>> [tuple(sam) in MN.pspace.domain.set for sam in samp_list] [True, True, True, True]
Changed in version 1.7.0: sample used to return an iterator containing the samples instead of value.
Changed in version 1.9.0: sample returns values or array of values instead of an iterator and numsamples is deprecated.
- sympy.stats.sample_iter(expr, condition=None, size=(), library='scipy', numsamples=oo, seed=None, **kwargs)[source]¶
Returns an iterator of realizations from the expression given a condition.
- Parameters
expr: Expr
Random expression to be realized
condition: Expr, optional
A conditional expression
size : int, tuple
Represents size of each sample in numsamples
numsamples: integer, optional
Length of the iterator (defaults to infinity)
seed :
An object to be used as seed by the given external library for sampling \(expr\). Following is the list of possible types of object for the supported libraries,
‘scipy’: int, numpy.random.RandomState, numpy.random.Generator
‘numpy’: int, numpy.random.RandomState, numpy.random.Generator
‘pymc3’: int
Optional, by default None, in which case seed settings related to the given library will be used. No modifications to environment’s global seed settings are done by this argument.
- Returns
sample_iter: iterator object
iterator object containing the sample/samples of given expr
Examples
>>> from sympy.stats import Normal, sample_iter >>> X = Normal('X', 0, 1) >>> expr = X*X + 3 >>> iterator = sample_iter(expr, numsamples=3) >>> list(iterator) [12, 4, 7]
See also
- sympy.stats.factorial_moment(X, n, condition=None, **kwargs)[source]¶
The factorial moment is a mathematical quantity defined as the expectation or average of the falling factorial of a random variable.
\[factorial-moment(X, n) = E(X(X - 1)(X - 2)...(X - n + 1))\]- Parameters
n: A natural number, n-th factorial moment.
condition : Expr containing RandomSymbols
A conditional expression.
Examples
>>> from sympy.stats import factorial_moment, Poisson, Binomial >>> from sympy import Symbol, S >>> lamda = Symbol('lamda') >>> X = Poisson('X', lamda) >>> factorial_moment(X, 2) lamda**2 >>> Y = Binomial('Y', 2, S.Half) >>> factorial_moment(Y, 2) 1/2 >>> factorial_moment(Y, 2, Y > 1) # find factorial moment for Y > 1 2
References
- sympy.stats.kurtosis(X, condition=None, **kwargs)[source]¶
Characterizes the tails/outliers of a probability distribution.
- Parameters
condition : Expr containing RandomSymbols
A conditional expression. kurtosis(X, X>0) is kurtosis of X given X > 0
Explanation
Kurtosis of any univariate normal distribution is 3. Kurtosis less than 3 means that the distribution produces fewer and less extreme outliers than the normal distribution.
\[kurtosis(X) = E(((X - E(X))/\sigma_X)^{4})\]Examples
>>> from sympy.stats import kurtosis, Exponential, Normal >>> from sympy import Symbol >>> X = Normal('X', 0, 1) >>> kurtosis(X) 3 >>> kurtosis(X, X > 0) # find kurtosis given X > 0 (-4/pi - 12/pi**2 + 3)/(1 - 2/pi)**2
>>> rate = Symbol('lamda', positive=True, real=True) >>> Y = Exponential('Y', rate) >>> kurtosis(Y) 9
References
- sympy.stats.skewness(X, condition=None, **kwargs)[source]¶
Measure of the asymmetry of the probability distribution.
- Parameters
condition : Expr containing RandomSymbols
A conditional expression. skewness(X, X>0) is skewness of X given X > 0
Explanation
Positive skew indicates that most of the values lie to the right of the mean.
\[skewness(X) = E(((X - E(X))/\sigma_X)^{3})\]Examples
>>> from sympy.stats import skewness, Exponential, Normal >>> from sympy import Symbol >>> X = Normal('X', 0, 1) >>> skewness(X) 0 >>> skewness(X, X > 0) # find skewness given X > 0 (-sqrt(2)/sqrt(pi) + 4*sqrt(2)/pi**(3/2))/(1 - 2/pi)**(3/2)
>>> rate = Symbol('lambda', positive=True, real=True) >>> Y = Exponential('Y', rate) >>> skewness(Y) 2
- sympy.stats.correlation(X, Y, condition=None, **kwargs)[source]¶
Correlation of two random expressions, also known as correlation coefficient or Pearson’s correlation.
Explanation
The normalized expectation that the two variables will rise and fall together
\[correlation(X,Y) = E((X-E(X))(Y-E(Y)) / (\sigma_x \sigma_y))\]Examples
>>> from sympy.stats import Exponential, correlation >>> from sympy import Symbol
>>> rate = Symbol('lambda', positive=True, real=True) >>> X = Exponential('X', rate) >>> Y = Exponential('Y', rate)
>>> correlation(X, X) 1 >>> correlation(X, Y) 0 >>> correlation(X, Y + rate*X) 1/sqrt(1 + lambda**(-2))
- sympy.stats.rv.sampling_density(expr, given_condition=None, library='scipy', numsamples=1, seed=None, **kwargs)[source]¶
Sampling version of density.
See also
- sympy.stats.rv.sampling_P(condition, given_condition=None, library='scipy', numsamples=1, evalf=True, seed=None, **kwargs)[source]¶
Sampling version of P.
See also
- sympy.stats.rv.sampling_E(expr, given_condition=None, library='scipy', numsamples=1, evalf=True, seed=None, **kwargs)[source]¶
Sampling version of E.
See also
- class sympy.stats.Moment(X, n, c=0, condition=None, **kwargs)[source]¶
Symbolic class for Moment
Examples
>>> from sympy import Symbol, Integral >>> from sympy.stats import Normal, Expectation, Probability, Moment >>> mu = Symbol('mu', real=True) >>> sigma = Symbol('sigma', positive=True) >>> X = Normal('X', mu, sigma) >>> M = Moment(X, 3, 1)
To evaluate the result of Moment use \(doit\):
>>> M.doit() mu**3 - 3*mu**2 + 3*mu*sigma**2 + 3*mu - 3*sigma**2 - 1
Rewrite the Moment expression in terms of Expectation:
>>> M.rewrite(Expectation) Expectation((X - 1)**3)
Rewrite the Moment expression in terms of Probability:
>>> M.rewrite(Probability) Integral((x - 1)**3*Probability(Eq(X, x)), (x, -oo, oo))
Rewrite the Moment expression in terms of Integral:
>>> M.rewrite(Integral) Integral(sqrt(2)*(X - 1)**3*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo))
- sympy.stats.moment(X, n, c=0, condition=None, *, evaluate=True, **kwargs)[source]¶
Return the nth moment of a random expression about c.
\[moment(X, c, n) = E((X-c)^{n})\]Default value of c is 0.
Examples
>>> from sympy.stats import Die, moment, E >>> X = Die('X', 6) >>> moment(X, 1, 6) -5/2 >>> moment(X, 2) 91/6 >>> moment(X, 1) == E(X) True
- class sympy.stats.CentralMoment(X, n, condition=None, **kwargs)[source]¶
Symbolic class Central Moment
Examples
>>> from sympy import Symbol, Integral >>> from sympy.stats import Normal, Expectation, Probability, CentralMoment >>> mu = Symbol('mu', real=True) >>> sigma = Symbol('sigma', positive=True) >>> X = Normal('X', mu, sigma) >>> CM = CentralMoment(X, 4)
To evaluate the result of CentralMoment use \(doit\):
>>> CM.doit().simplify() 3*sigma**4
Rewrite the CentralMoment expression in terms of Expectation:
>>> CM.rewrite(Expectation) Expectation((X - Expectation(X))**4)
Rewrite the CentralMoment expression in terms of Probability:
>>> CM.rewrite(Probability) Integral((x - Integral(x*Probability(True), (x, -oo, oo)))**4*Probability(Eq(X, x)), (x, -oo, oo))
Rewrite the CentralMoment expression in terms of Integral:
>>> CM.rewrite(Integral) Integral(sqrt(2)*(X - Integral(sqrt(2)*X*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo)))**4*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo))
- sympy.stats.cmoment(X, n, condition=None, *, evaluate=True, **kwargs)[source]¶
Return the nth central moment of a random expression about its mean.
\[cmoment(X, n) = E((X - E(X))^{n})\]Examples
>>> from sympy.stats import Die, cmoment, variance >>> X = Die('X', 6) >>> cmoment(X, 3) 0 >>> cmoment(X, 2) 35/12 >>> cmoment(X, 2) == variance(X) True
- class sympy.stats.ExpectationMatrix(expr, condition=None)[source]¶
Expectation of a random matrix expression.
Examples
>>> from sympy.stats import ExpectationMatrix, Normal >>> from sympy.stats.rv import RandomMatrixSymbol >>> from sympy import symbols, MatrixSymbol, Matrix >>> k = symbols("k") >>> A, B = MatrixSymbol("A", k, k), MatrixSymbol("B", k, k) >>> X, Y = RandomMatrixSymbol("X", k, 1), RandomMatrixSymbol("Y", k, 1) >>> ExpectationMatrix(X) ExpectationMatrix(X) >>> ExpectationMatrix(A*X).shape (k, 1)
To expand the expectation in its expression, use
expand():>>> ExpectationMatrix(A*X + B*Y).expand() A*ExpectationMatrix(X) + B*ExpectationMatrix(Y) >>> ExpectationMatrix((X + Y)*(X - Y).T).expand() ExpectationMatrix(X*X.T) - ExpectationMatrix(X*Y.T) + ExpectationMatrix(Y*X.T) - ExpectationMatrix(Y*Y.T)
To evaluate the
ExpectationMatrix, usedoit():>>> N11, N12 = Normal('N11', 11, 1), Normal('N12', 12, 1) >>> N21, N22 = Normal('N21', 21, 1), Normal('N22', 22, 1) >>> M11, M12 = Normal('M11', 1, 1), Normal('M12', 2, 1) >>> M21, M22 = Normal('M21', 3, 1), Normal('M22', 4, 1) >>> x1 = Matrix([[N11, N12], [N21, N22]]) >>> x2 = Matrix([[M11, M12], [M21, M22]]) >>> ExpectationMatrix(x1 + x2).doit() Matrix([ [12, 14], [24, 26]])
- class sympy.stats.VarianceMatrix(arg, condition=None)[source]¶
Variance of a random matrix probability expression. Also known as Covariance matrix, auto-covariance matrix, dispersion matrix, or variance-covariance matrix.
Examples
>>> from sympy.stats import VarianceMatrix >>> from sympy.stats.rv import RandomMatrixSymbol >>> from sympy import symbols, MatrixSymbol >>> k = symbols("k") >>> A, B = MatrixSymbol("A", k, k), MatrixSymbol("B", k, k) >>> X, Y = RandomMatrixSymbol("X", k, 1), RandomMatrixSymbol("Y", k, 1) >>> VarianceMatrix(X) VarianceMatrix(X) >>> VarianceMatrix(X).shape (k, k)
To expand the variance in its expression, use
expand():>>> VarianceMatrix(A*X).expand() A*VarianceMatrix(X)*A.T >>> VarianceMatrix(A*X + B*Y).expand() 2*A*CrossCovarianceMatrix(X, Y)*B.T + A*VarianceMatrix(X)*A.T + B*VarianceMatrix(Y)*B.T
- class sympy.stats.CrossCovarianceMatrix(arg1, arg2, condition=None)[source]¶
Covariance of a random matrix probability expression.
Examples
>>> from sympy.stats import CrossCovarianceMatrix >>> from sympy.stats.rv import RandomMatrixSymbol >>> from sympy import symbols, MatrixSymbol >>> k = symbols("k") >>> A, B = MatrixSymbol("A", k, k), MatrixSymbol("B", k, k) >>> C, D = MatrixSymbol("C", k, k), MatrixSymbol("D", k, k) >>> X, Y = RandomMatrixSymbol("X", k, 1), RandomMatrixSymbol("Y", k, 1) >>> Z, W = RandomMatrixSymbol("Z", k, 1), RandomMatrixSymbol("W", k, 1) >>> CrossCovarianceMatrix(X, Y) CrossCovarianceMatrix(X, Y) >>> CrossCovarianceMatrix(X, Y).shape (k, k)
To expand the covariance in its expression, use
expand():>>> CrossCovarianceMatrix(X + Y, Z).expand() CrossCovarianceMatrix(X, Z) + CrossCovarianceMatrix(Y, Z) >>> CrossCovarianceMatrix(A*X, Y).expand() A*CrossCovarianceMatrix(X, Y) >>> CrossCovarianceMatrix(A*X, B.T*Y).expand() A*CrossCovarianceMatrix(X, Y)*B >>> CrossCovarianceMatrix(A*X + B*Y, C.T*Z + D.T*W).expand() A*CrossCovarianceMatrix(X, W)*D + A*CrossCovarianceMatrix(X, Z)*C + B*CrossCovarianceMatrix(Y, W)*D + B*CrossCovarianceMatrix(Y, Z)*C
Mechanics¶
SymPy Stats employs a relatively complex class hierarchy.
RandomDomains are a mapping of variables to possible values.
For example, we might say that the symbol Symbol('x') can
take on the values \(\{1,2,3,4,5,6\}\).
A PSpace, or Probability Space, combines a RandomDomain
with a density to provide probabilistic information. For example
the above domain could be enhanced by a finite density
{1:1/6, 2:1/6, 3:1/6, 4:1/6, 5:1/6, 6:1/6} to
fully define the roll of a fair die named x.
A RandomSymbol represents the PSpace’s symbol ‘x’ inside of SymPy expressions.
The RandomDomain and PSpace classes are almost never directly instantiated. Instead they are subclassed for a variety of situations.
RandomDomains and PSpaces must be sufficiently general to represent domains and spaces of several variables with arbitrarily complex densities. This generality is often unnecessary. Instead we often build SingleDomains and SinglePSpaces to represent single, univariate events and processes such as a single die or a single normal variable.
Another common case is to collect together a set of such univariate random variables. A collection of independent SinglePSpaces or SingleDomains can be brought together to form a ProductDomain or ProductPSpace. These objects would be useful in representing three dice rolled together for example.
The Conditional adjective is added whenever we add a global condition to a RandomDomain or PSpace. A common example would be three independent dice where we know their sum to be greater than 12.
We specialize further into Finite and Continuous versions of these classes to represent finite (such as dice) and continuous (such as normals) random variables.
Additionally there are a few specialized classes that implement certain common random variable types. There is for example a DiePSpace that implements SingleFinitePSpace and a NormalPSpace that implements SingleContinuousPSpace.
- class sympy.stats.frv_types.DiePSpace¶
- class sympy.stats.crv_types.NormalPSpace¶
RandomVariables can be extracted from these objects using the PSpace.values method.
As previously mentioned SymPy Stats employs a relatively complex class structure. Inheritance is widely used in the implementation of end-level classes. This tactic was chosen to balance between the need to allow SymPy to represent arbitrarily defined random variables and optimizing for common cases. This complicates the code but is structured to only be important to those working on extending SymPy Stats to other random variable types.
Users will not use this class structure. Instead these mechanics are exposed through variable creation functions Die, Coin, FiniteRV, Normal, Exponential, etc…. These build the appropriate SinglePSpaces and return the corresponding RandomVariable. Conditional and Product spaces are formed in the natural construction of SymPy expressions and the use of interface functions E, Given, Density, etc….
- sympy.stats.Die()¶
- sympy.stats.Normal()¶
There are some additional functions that may be useful. They are largely used internally.
- sympy.stats.rv.pspace(expr)[source]¶
Returns the underlying Probability Space of a random expression.
For internal use.
Examples
>>> from sympy.stats import pspace, Normal >>> X = Normal('X', 0, 1) >>> pspace(2*X + 1) == X.pspace True
- sympy.stats.rv.rs_swap(a, b)[source]¶
Build a dictionary to swap RandomSymbols based on their underlying symbol.
i.e. if
X = ('x', pspace1)andY = ('x', pspace2)thenXandYmatch and the key, value pair{X:Y}will appear in the resultInputs: collections a and b of random variables which share common symbols Output: dict mapping RVs in a to RVs in b