MarginalApprox.predict(Xnew, point=None, diag=False, pred_noise=False, given=None, jitter=1e-06, 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.


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

pointpymc.Point, optional

A specific point to condition on.

diagbool, default False

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

pred_noisebool, default False

Whether or not observation noise is included in the conditional.

givendict, optional

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

jitterfloat, default 1e-6

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

modelModel, optional

Model with the Gaussian Process component for which predictions will be generated. It is optional when inside a with context, otherwise it is required.