# jax.scipy.special.gammaincÂ¶

jax.scipy.special.gammainc(a, x)[source]Â¶

Regularized lower incomplete gamma function.

LAX-backend implementation of gammainc(). Original docstring below.

gammainc(x1, x2, /, out=None, *, where=True, casting=â€™same_kindâ€™, order=â€™Kâ€™, dtype=None, subok=True[, signature, extobj])

gammainc(a, x)

It is defined as

$P(a, x) = \frac{1}{\Gamma(a)} \int_0^x t^{a - 1}e^{-t} dt$

for $$a > 0$$ and $$x \geq 0$$. See [dlmf] for details.

Parameters
• a (array_like) â€“ Positive parameter

• x (array_like) â€“ Nonnegative argument

Returns

Values of the lower incomplete gamma function

Return type

scalar or ndarray

Notes

The function satisfies the relation gammainc(a, x) + gammaincc(a, x) = 1 where gammaincc is the regularized upper incomplete gamma function.

The implementation largely follows that of [boost].

gammaincc()

regularized upper incomplete gamma function

gammaincinv()

inverse of the regularized lower incomplete gamma function with respect to x

gammainccinv()

inverse of the regularized upper incomplete gamma function with respect to x

References

dlmf

NIST Digital Library of Mathematical functions https://dlmf.nist.gov/8.2#E4

boost

Maddock et. al., â€śIncomplete Gamma Functionsâ€ť, https://www.boost.org/doc/libs/1_61_0/libs/math/doc/html/math_toolkit/sf_gamma/igamma.html

Examples

>>> import scipy.special as sc


It is the CDF of the gamma distribution, so it starts at 0 and monotonically increases to 1.

>>> sc.gammainc(0.5, [0, 1, 10, 100])
array([0.        , 0.84270079, 0.99999226, 1.        ])


It is equal to one minus the upper incomplete gamma function.

>>> a, x = 0.5, 0.4
>>> sc.gammainc(a, x)
0.6289066304773024
>>> 1 - sc.gammaincc(a, x)
0.6289066304773024