jax.scipy.stats.multivariate_normal.logpdf

jax.scipy.stats.multivariate_normal.logpdf(x, mean, cov, allow_singular=None)

Multivariate normal log probability distribution function.

JAX implementation of scipy.stats.multivariate_normal logpdf.

The multivariate normal PDF is defined as

\[f(x) = \frac{1}{(2\pi)^k\det\Sigma}\exp\left(-\frac{(x-\mu)^T\Sigma^{-1}(x-\mu)}{2} \right)\]

where \(\mu\) is the mean, \(\Sigma\) is the covarance matrix (cov), and \(k\) is the rank of \(\Sigma\).

Parameters:

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

• mean (Array | ndarray | bool_ | number | bool | int | float | complex) – arraylike, centroid of distribution

• cov (Array | ndarray | bool_ | number | bool | int | float | complex) – arraylike, covariance matrix of distribution

• allow_singular (None) – not supported

Returns:

array of logpdf values.

Return type:

Array | ndarray | bool_ | number | bool | int | float | complex

See also

jax.scipy.stats.multivariate_normal.pdf()