pymc.gp.MarginalKron.predict

MarginalKron.predict(Xnew, point=None, diag=False, pred_noise=False, model=None)

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.

Parameters

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.