jax.scipy.stats.chi2.logsf

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

Chi-square log survival function.

JAX implementation of scipy.stats.chi2 logsf.

The survival function is defined as

\[f_{sf}(x, k) = 1 - f_{cdf}(x, k)\]

where \(f_{cdf}(x, k)\) is the cumulative distribution function, jax.scipy.stats.chi2.cdf(). 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 SF

• 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 logsf 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.logpdf()