jax.scipy.stats.chi2.logpdf

jax.scipy.stats.chi2.logpdf(x, df, loc=0, scale=1)

Chi-square log probability distribution function.

JAX implementation of scipy.stats.chi2 logpdf.

The chi-square probability distribution function is given by:

\[\begin{split}f(x, k) = \begin{cases} \frac{x^{k/2-1}e^{-x/2}}{2^{k/2}\Gamma(k/2)} & x \ge 0 \\ 0 & \mathrm{otherwise} \end{cases}\end{split}\]

for \(k\) degrees of freedom, and where \(\Gamma\) is the gamma() function. JAX follows the scipy convention of using df to denote degrees of freedom.

Parameters:

• x (Array | ndarray | bool_ | number | bool | int | float | complex) – arraylike, value at which to evaluate the PDF

• df (Array | ndarray | bool_ | number | bool | int | float | complex) – arraylike, distribution shape parameter

• loc (Array | ndarray | bool_ | number | bool | int | float | complex) – arraylike, distribution offset parameter

• scale (Array | ndarray | bool_ | number | bool | int | float | complex) – arraylike, distribution scale parameter

Returns:

array of logpdf values.

Return type:

Array

See also

• jax.scipy.stats.chi2.cdf()

• jax.scipy.stats.chi2.pdf()

• jax.scipy.stats.chi2.sf()

• jax.scipy.stats.chi2.logcdf()

• jax.scipy.stats.chi2.logsf()