Latent.prior(name, X, reparameterize=True, jitter=1e-06, **kwargs)[source]#

Returns the GP prior distribution evaluated over the input locations X.

This is the prior probability over the space of functions described by its mean and covariance function.

\[f \mid X \sim \text{MvNormal}\left( \mu(X), k(X, X') \right)\]

Name of the random variable


Function input values. If one-dimensional, must be a column vector with shape (n, 1).

reparameterizebool, default True

Reparameterize the distribution by rotating the random variable by the Cholesky factor of the covariance matrix.

jitterfloat, default 1e-6

A small correction added to the diagonal of positive semi-definite covariance matrices to ensure numerical stability.


Extra keyword arguments that are passed to MvNormal distribution constructor.