Marginal.predict(Xnew, point=None, diag=False, pred_noise=False, given=None, jitter=1e-06, model=None)[source]#

Return the mean vector and covariance matrix of the conditional distribution as numpy arrays, given a point, such as the MAP estimate or a sample from a trace.

Xnew: array-like

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

point: pymc.model.Point

A specific point to condition on.

diag: bool

If True, return the diagonal instead of the full covariance matrix. Default is False.

pred_noise: bool

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

given: dict

Same as conditional method.

jitter: scalar

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