# pymc.gp.Latent.prior#

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.

$f \mid X \sim \text{MvNormal}\left( \mu(X), k(X, X') \right)$
Parameters
name: string

Name of the random variable

X: array-like

Function input values.

reparameterize: bool

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

jitter: scalar

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

**kwargs

Extra keyword arguments that are passed to distribution constructor.