Marginal.conditional(name, Xnew, pred_noise=False, given=None, jitter=1e-06, **kwargs)[source]#

Returns the conditional distribution evaluated over new input locations Xnew.

Given a set of function values f that the GP prior was over, the conditional distribution over a set of new points, f_* is:

\[f_* \mid f, X, X_* \sim \mathcal{GP}\left( K(X_*, X) [K(X, X) + K_{n}(X, X)]^{-1} f \,, K(X_*, X_*) - K(X_*, X) [K(X, X) + K_{n}(X, X)]^{-1} K(X, X_*) \right)\]
name: string

Name of the random variable

Xnew: array-like

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

pred_noise: bool

Whether or not observation noise is included in the conditional. Default is False.

given: dict

Can optionally take as key value pairs: X, y, sigma, and gp. See the section in the documentation on additive GP models in PyMC for more information.

jitter: scalar

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.