jax.scipy.stats.beta.logsf#

jax.scipy.stats.beta.logsf(x, a, b, loc=0, scale=1)[source]#

Beta distribution log survival function.

JAX implementation of scipy.stats.beta logsf.

The survival function is defined as

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

where \(f_{cdf}(x, a, b)\) is the beta cumulative distribution function, jax.scipy.stats.beta.cdf().

Parameters:

x (Array | ndarray | bool_ | number | bool | int | float | complex) – arraylike, value at which to evaluate the SF
a (Array | ndarray | bool_ | number | bool | int | float | complex) – arraylike, distribution shape parameter
b (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

jax.scipy.stats.beta.logsf