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# scipy.stats.erlang

`scipy.stats.``erlang` = <scipy.stats._continuous_distns.erlang_gen object>

  An Erlang continuous random variable.

  As an instance of the `rv_continuous` class, `erlang` 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.

  See also

  `gamma`

  Notes

  The Erlang distribution is a special case of the Gamma distribution, with the shape parameter a an integer. Note that this restriction is not enforced by `erlang`. It will, however, generate a warning the first time a non-integer value is used for the shape parameter.

  Refer to `gamma` for examples.

  Methods

  `rvs(a, loc=0, scale=1, size=1, random_state=None)` | Random variates.
  `pdf(x, a, loc=0, scale=1)` | Probability density function.
  `logpdf(x, a, loc=0, scale=1)` | Log of the probability density function.
  `cdf(x, a, loc=0, scale=1)` | Cumulative density function.
  `logcdf(x, a, loc=0, scale=1)` | Log of the cumulative density function.
  `sf(x, a, loc=0, scale=1)` | Survival function (also defined as `1 - cdf`, but sf is sometimes more accurate).
  `logsf(x, a, loc=0, scale=1)` | Log of the survival function.
  `ppf(q, a, loc=0, scale=1)` | Percent point function (inverse of `cdf` — percentiles).
  `isf(q, a, loc=0, scale=1)` | Inverse survival function (inverse of `sf`).
  `moment(n, a, loc=0, scale=1)` | Non-central moment of order n
  `stats(a, loc=0, scale=1, moments='mv')` | Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
  `entropy(a, loc=0, scale=1)` | (Differential) entropy of the RV.
  `fit(data, a, loc=0, scale=1)` | Parameter estimates for generic data.
  `expect(func, args=(a,), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds)` | Expected value of a function (of one argument) with respect to the distribution.
  `median(a, loc=0, scale=1)` | Median of the distribution.
  `mean(a, loc=0, scale=1)` | Mean of the distribution.
  `var(a, loc=0, scale=1)` | Variance of the distribution.
  `std(a, loc=0, scale=1)` | Standard deviation of the distribution.
  `interval(alpha, a, loc=0, scale=1)` | Endpoints of the range that contains alpha percent of the distribution