jax.scipy.stats.expon.logsf

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

Exponential log survival function.

JAX implementation of scipy.stats.expon logsf.

The survival function is defined as

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

where \(f_{cdf}(x)\) is the exponential cumulative distribution function, jax.scipy.stats.expon.cdf().

Parameters:

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

• 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 pdf values.

Return type:

Array

See also

jax.scipy.stats.expon.cdf() jax.scipy.stats.expon.pdf() jax.scipy.stats.expon.ppf() jax.scipy.stats.expon.sf() jax.scipy.stats.expon.logcdf() jax.scipy.stats.expon.logpdf() jax.scipy.stats.expon.logsf()