pymc.gp.MarginalApprox.conditional#

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

Return the approximate conditional distribution of the GP evaluated over new input locations Xnew.

Parameters:
namestr

Name of the random variable

Xnewarray_like

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

pred_noisebool, default False

Whether or not observation noise is included in the conditional.

givendict, optional

Can take key value pairs: X, Xu, 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.

**kwargs

Extra keyword arguments that are passed to MvNormal distribution constructor.