# scipy.stats.exponpow¶

scipy.stats.exponpow = <scipy.stats._continuous_distns.exponpow_gen object>[source]

An exponential power continuous random variable.

As an instance of the rv_continuous class, exponpow 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.

Notes

The probability density function for exponpow is:

$f(x, b) = b x^{b-1} \exp(1 + x^b - \exp(x^b))$

for $$x \ge 0$$, $$b > 0$$. Note that this is a different distribution from the exponential power distribution that is also known under the names “generalized normal” or “generalized Gaussian”.

exponpow takes b as a shape parameter for $$b$$.

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, exponpow.pdf(x, b, loc, scale) is identically equivalent to exponpow.pdf(y, b) / scale with y = (x - loc) / scale.

References

http://www.math.wm.edu/~leemis/chart/UDR/PDFs/Exponentialpower.pdf

Examples

>>> from scipy.stats import exponpow
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)


Calculate a few first moments:

>>> b = 2.7
>>> mean, var, skew, kurt = exponpow.stats(b, moments='mvsk')


Display the probability density function (pdf):

>>> x = np.linspace(exponpow.ppf(0.01, b),
...                 exponpow.ppf(0.99, b), 100)
>>> ax.plot(x, exponpow.pdf(x, b),
...        'r-', lw=5, alpha=0.6, label='exponpow pdf')


Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

Freeze the distribution and display the frozen pdf:

>>> rv = exponpow(b)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')


Check accuracy of cdf and ppf:

>>> vals = exponpow.ppf([0.001, 0.5, 0.999], b)
>>> np.allclose([0.001, 0.5, 0.999], exponpow.cdf(vals, b))
True


Generate random numbers:

>>> r = exponpow.rvs(b, size=1000)


And compare the histogram:

>>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()


Methods

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

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