numpy.random.RandomState.noncentral_chisquare¶

method

RandomState.noncentral_chisquare(df, nonc, size=None)¶

Draw samples from a noncentral chi-square distribution.

The noncentral

System Message: WARNING/2 (\chi^2)

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distribution is a generalization of the

System Message: WARNING/2 (\chi^2)

distribution.

Note

New code should use the noncentral_chisquare method of a default_rng() instance instead; see random-quick-start.

Parameters

dffloat or array_like of floats: Degrees of freedom, must be > 0.

Changed in version 1.10.0: Earlier NumPy versions required dfnum > 1.
noncfloat or array_like of floats: Non-centrality, must be non-negative.
sizeint or tuple of ints, optional: Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if df and nonc are both scalars. Otherwise, np.broadcast(df, nonc).size samples are drawn.

Returns

outndarray or scalar: Drawn samples from the parameterized noncentral chi-square distribution.

See also

Generator.noncentral_chisquare: which should be used for new code.

Notes

The probability density function for the noncentral Chi-square distribution is

System Message: WARNING/2 (P(x;df,nonc) = \sum^{\infty}_{i=0} \frac{e^{-nonc/2}(nonc/2)^{i}}{i!} P_{Y_{df+2i}}(x), )

where

System Message: WARNING/2 (Y_{q})

is the Chi-square with q degrees of freedom.

References

1: Wikipedia, “Noncentral chi-squared distribution” https://en.wikipedia.org/wiki/Noncentral_chi-squared_distribution

Examples

Draw values from the distribution and plot the histogram

>>> import matplotlib.pyplot as plt
>>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),
...                   bins=200, density=True)
>>> plt.show()

../../../_images/numpy-random-RandomState-noncentral_chisquare-1_00_00.png

Draw values from a noncentral chisquare with very small noncentrality, and compare to a chisquare.

>>> plt.figure()
>>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),
...                   bins=np.arange(0., 25, .1), density=True)
>>> values2 = plt.hist(np.random.chisquare(3, 100000),
...                    bins=np.arange(0., 25, .1), density=True)
>>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')
>>> plt.show()

../../../_images/numpy-random-RandomState-noncentral_chisquare-1_01_00.png

Demonstrate how large values of non-centrality lead to a more symmetric distribution.

>>> plt.figure()
>>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),
...                   bins=200, density=True)
>>> plt.show()

../../../_images/numpy-random-RandomState-noncentral_chisquare-1_02_00.png

numpy.random.RandomState.negative_binomial numpy.random.RandomState.noncentral_f

numpy.random.RandomState.noncentral_chisquare¶

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