jax.scipy.stats.bernoulli.logpmf

jax.scipy.stats.bernoulli.logpmf(k, p, loc=0)

Bernoulli log probability mass function.

JAX implementation of scipy.stats.bernoulli logpmf

The Bernoulli probability mass function is defined as

\[\begin{split}f(k) = \begin{cases} 1 - p, & k = 0 \\ p, & k = 1 \\ 0, & \mathrm{otherwise} \end{cases}\end{split}\]

Parameters:

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

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

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

Returns:

array of logpmf values

Return type:

Array

See also

• jax.scipy.stats.bernoulli.cdf()

• jax.scipy.stats.bernoulli.pmf()

• jax.scipy.stats.bernoulli.ppf()