scipy.stats.invgauss

scipy.stats.invgauss = <scipy.stats._continuous_distns.invgauss_gen object at 0x7f6169cd6650>

An inverse Gaussian continuous random variable.

Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:

Parameters:

x : array_like

quantiles

q : array_like

lower or upper tail probability

mu : array_like

shape parameters

loc : array_like, optional

location parameter (default=0)

scale : array_like, optional

scale parameter (default=1)

size : int or tuple of ints, optional

shape of random variates (default computed from input arguments )

moments : str, optional

composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. Default is ‘mv’.

Alternatively, the object may be called (as a function) to fix the shape,

location, and scale parameters returning a “frozen” continuous RV object:

rv = invgauss(mu, loc=0, scale=1)

• Frozen RV object with the same methods but holding the given shape, location, and scale fixed.

Notes

The probability density function for invgauss is:

invgauss.pdf(x, mu) = 1 / sqrt(2*pi*x**3) * exp(-(x-mu)**2/(2*x*mu**2))

for x > 0.

When mu is too small, evaluating the cumulative density function will be inaccurate due to cdf(mu -> 0) = inf * 0. NaNs are returned for mu <= 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 = 0.145462645553
>>> 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, 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, normed=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()

(Source code)

Methods

rvs(mu, loc=0, scale=1, size=1)	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 density function.
logcdf(x, mu, loc=0, scale=1)	Log of the cumulative density function.
sf(x, mu, loc=0, scale=1)	Survival function (1-cdf — 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, mu, loc=0, scale=1)	Parameter estimates for generic data.
expect(func, 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