# scipy.stats.halfgennorm#

scipy.stats.halfgennorm = <scipy.stats._continuous_distns.halfgennorm_gen object>[source]#

The upper half of a generalized normal continuous random variable.

As an instance of the rv_continuous class, halfgennorm 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.

Methods

 rvs(beta, loc=0, scale=1, size=1, random_state=None) Random variates. pdf(x, beta, loc=0, scale=1) Probability density function. logpdf(x, beta, loc=0, scale=1) Log of the probability density function. cdf(x, beta, loc=0, scale=1) Cumulative distribution function. logcdf(x, beta, loc=0, scale=1) Log of the cumulative distribution function. sf(x, beta, loc=0, scale=1) Survival function (also defined as 1 - cdf, but sf is sometimes more accurate). logsf(x, beta, loc=0, scale=1) Log of the survival function. ppf(q, beta, loc=0, scale=1) Percent point function (inverse of cdf — percentiles). isf(q, beta, loc=0, scale=1) Inverse survival function (inverse of sf). moment(order, beta, loc=0, scale=1) Non-central moment of the specified order. stats(beta, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(beta, loc=0, scale=1) (Differential) entropy of the RV. fit(data) Parameter estimates for generic data. See scipy.stats.rv_continuous.fit for detailed documentation of the keyword arguments. expect(func, args=(beta,), 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(beta, loc=0, scale=1) Median of the distribution. mean(beta, loc=0, scale=1) Mean of the distribution. var(beta, loc=0, scale=1) Variance of the distribution. std(beta, loc=0, scale=1) Standard deviation of the distribution. interval(confidence, beta, loc=0, scale=1) Confidence interval with equal areas around the median.

gennorm

generalized normal distribution

expon

exponential distribution

halfnorm

half normal distribution

Notes

The probability density function for halfgennorm is:

$f(x, \beta) = \frac{\beta}{\Gamma(1/\beta)} \exp(-|x|^\beta)$

for $$x, \beta > 0$$. $$\Gamma$$ is the gamma function (scipy.special.gamma).

halfgennorm takes beta as a shape parameter for $$\beta$$. For $$\beta = 1$$, it is identical to an exponential distribution. For $$\beta = 2$$, it is identical to a half normal distribution (with scale=1/sqrt(2)).

References

[1]

“Generalized normal distribution, Version 1”, https://en.wikipedia.org/wiki/Generalized_normal_distribution#Version_1

Examples

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


Calculate the first four moments:

>>> beta = 0.675
>>> mean, var, skew, kurt = halfgennorm.stats(beta, moments='mvsk')


Display the probability density function (pdf):

>>> x = np.linspace(halfgennorm.ppf(0.01, beta),
...                 halfgennorm.ppf(0.99, beta), 100)
>>> ax.plot(x, halfgennorm.pdf(x, beta),
...        'r-', lw=5, alpha=0.6, label='halfgennorm 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 = halfgennorm(beta)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')


Check accuracy of cdf and ppf:

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


Generate random numbers:

>>> r = halfgennorm.rvs(beta, size=1000)


And compare the histogram:

>>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
>>> ax.set_xlim([x[0], x[-1]])
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()