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

expect.__doc__

        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.