# scipy.stats.invgauss¶

scipy.stats.invgauss(*args, **kwds) = <scipy.stats._continuous_distns.invgauss_gen object>[source]

An inverse Gaussian continuous random variable.

As an instance of the rv_continuous class, invgauss 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 invgauss is:

$f(x, \mu) = \frac{1}{\sqrt{2 \pi x^3}} \exp(-\frac{(x-\mu)^2}{2 x \mu^2})$

for $$x >= 0$$ and $$\mu > 0$$.

invgauss takes mu as a shape parameter for $$\mu$$.

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, invgauss.pdf(x, mu, loc, scale) is identically equivalent to invgauss.pdf(y, mu) / scale with y = (x - loc) / scale. Note that shifting the location of a distribution does not make it a “noncentral” distribution; noncentral generalizations of some distributions are available in separate classes.

When $$\mu$$ is too small, evaluating the cumulative distribution function will be inaccurate due to cdf(mu -> 0) = inf * 0. NaNs are returned for $$\mu \le 0.0028$$.

Examples

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

Calculate a few first moments:

>>> mu = 0.145
>>> mean, var, skew, kurt = invgauss.stats(mu, moments='mvsk')

Display the probability density function (pdf):

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

Check accuracy of cdf and ppf:

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

Generate random numbers:

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

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