Module « scipy.stats »

Fonction bernoulli - module scipy.stats

Signature de la fonction bernoulli

def bernoulli(*args, **kwds) 

Description

bernoulli.__doc__

A Bernoulli discrete random variable.

    As an instance of the `rv_discrete` class, `bernoulli` object inherits from it
    a collection of generic methods (see below for the full list),
    and completes them with details specific for this particular distribution.
    
    Methods
    -------
    rvs(p, loc=0, size=1, random_state=None)
        Random variates.
    pmf(k, p, loc=0)
        Probability mass function.
    logpmf(k, p, loc=0)
        Log of the probability mass function.
    cdf(k, p, loc=0)
        Cumulative distribution function.
    logcdf(k, p, loc=0)
        Log of the cumulative distribution function.
    sf(k, p, loc=0)
        Survival function  (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).
    logsf(k, p, loc=0)
        Log of the survival function.
    ppf(q, p, loc=0)
        Percent point function (inverse of ``cdf`` --- percentiles).
    isf(q, p, loc=0)
        Inverse survival function (inverse of ``sf``).
    stats(p, loc=0, moments='mv')
        Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
    entropy(p, loc=0)
        (Differential) entropy of the RV.
    expect(func, args=(p,), loc=0, lb=None, ub=None, conditional=False)
        Expected value of a function (of one argument) with respect to the distribution.
    median(p, loc=0)
        Median of the distribution.
    mean(p, loc=0)
        Mean of the distribution.
    var(p, loc=0)
        Variance of the distribution.
    std(p, loc=0)
        Standard deviation of the distribution.
    interval(alpha, p, loc=0)
        Endpoints of the range that contains fraction alpha [0, 1] of the
        distribution

    Notes
    -----
    The probability mass function for `bernoulli` is:

    .. math::

       f(k) = \begin{cases}1-p  &\text{if } k = 0\\
                           p    &\text{if } k = 1\end{cases}

    for :math:`k` in :math:`\{0, 1\}`, :math:`0 \leq p \leq 1`

    `bernoulli` takes :math:`p` as shape parameter,
    where :math:`p` is the probability of a single success
    and :math:`1-p` is the probability of a single failure.

    The probability mass function above is defined in the "standardized" form.
    To shift distribution use the ``loc`` parameter.
    Specifically, ``bernoulli.pmf(k, p, loc)`` is identically
    equivalent to ``bernoulli.pmf(k - loc, p)``.

    Examples
    --------
    >>> from scipy.stats import bernoulli
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)
    
    Calculate the first four moments:
    
    >>> p = 0.3
    >>> mean, var, skew, kurt = bernoulli.stats(p, moments='mvsk')
    
    Display the probability mass function (``pmf``):
    
    >>> x = np.arange(bernoulli.ppf(0.01, p),
    ...               bernoulli.ppf(0.99, p))
    >>> ax.plot(x, bernoulli.pmf(x, p), 'bo', ms=8, label='bernoulli pmf')
    >>> ax.vlines(x, 0, bernoulli.pmf(x, p), colors='b', lw=5, alpha=0.5)
    
    Alternatively, the distribution object can be called (as a function)
    to fix the shape and location. This returns a "frozen" RV object holding
    the given parameters fixed.
    
    Freeze the distribution and display the frozen ``pmf``:
    
    >>> rv = bernoulli(p)
    >>> ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1,
    ...         label='frozen pmf')
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()
    
    Check accuracy of ``cdf`` and ``ppf``:
    
    >>> prob = bernoulli.cdf(x, p)
    >>> np.allclose(x, bernoulli.ppf(prob, p))
    True
    
    Generate random numbers:
    
    >>> r = bernoulli.rvs(p, size=1000)