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Classe « rv_discrete »

Méthode scipy.stats.rv_discrete.expect

Signature de la méthode expect

def expect(self, func=None, args=(), loc=0, lb=None, ub=None, conditional=False, maxcount=1000, tolerance=1e-10, chunksize=32) 

Description

help(rv_discrete.expect)

Calculate expected value of a function with respect to the distribution
for discrete distribution by numerical summation.

Parameters
----------
func : callable, optional
    Function for which the expectation value is calculated.
    Takes only one argument.
    The default is the identity mapping f(k) = k.
args : tuple, optional
    Shape parameters of the distribution.
loc : float, optional
    Location parameter.
    Default is 0.
lb, ub : int, optional
    Lower and upper bound for the summation, default is set to the
    support of the distribution, inclusive (``lb <= k <= ub``).
conditional : bool, optional
    If true then the expectation is corrected by the conditional
    probability of the summation interval. The return value is the
    expectation of the function, `func`, conditional on being in
    the given interval (k such that ``lb <= k <= ub``).
    Default is False.
maxcount : int, optional
    Maximal number of terms to evaluate (to avoid an endless loop for
    an infinite sum). Default is 1000.
tolerance : float, optional
    Absolute tolerance for the summation. Default is 1e-10.
chunksize : int, optional
    Iterate over the support of a distributions in chunks of this size.
    Default is 32.

Returns
-------
expect : float
    Expected value.

Notes
-----
For heavy-tailed distributions, the expected value may or
may not exist,
depending on the function, `func`. If it does exist, but the
sum converges
slowly, the accuracy of the result may be rather low. For instance, for
``zipf(4)``, accuracy for mean, variance in example is only 1e-5.
increasing `maxcount` and/or `chunksize` may improve the result,
but may also make zipf very slow.

The function is not vectorized.

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