# statsmodels.distributions.discrete.DiscretizedCount.expect¶

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

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`).

conditionalbool, `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.