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All Samples(4)  |  Call(3)  |  Derive(0)  |  Import(1)
Create a Finite Random Variable representing a Bernoulli process.

Returns a RandomSymbol

>>> 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}(more...)

        def Bernoulli(name, p, succ=1, fail=0):
    """
    Create a Finite Random Variable representing a Bernoulli process.

    Returns a RandomSymbol

    >>> 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}
    """

    return rv(name, BernoulliDistribution, p, succ, fail)
        


src/s/y/sympy-HEAD/sympy/stats/tests/test_finite_rv.py   sympy(Download)
from sympy import (EmptySet, FiniteSet, S, Symbol, Interval, exp, erf, sqrt,
        symbols, simplify, Eq, cos, And, Tuple, Or, Dict, sympify, binomial,
        factor)
from sympy.stats import (DiscreteUniform, Die, Bernoulli, Coin, Binomial,
        Hypergeometric, P, E, variance, covariance, skewness, sample, density,
def test_bernoulli():
    p, a, b = symbols('p a b')
    X = Bernoulli('B', p, a, b)
 
    assert E(X) == a*p + b*(-p + 1)
    assert density(X)[a] == p
    assert density(X)[b] == 1 - p