pymc.gp.LatentKron.conditional

LatentKron.conditional(name, Xnew, jitter=1e-06, **kwargs)

Return the conditional distribution evaluated over new input locations Xnew.

Xnew will be split by columns and fed to the relevant covariance functions based on their input_dim. For example, if cov_func1, cov_func2, and cov_func3 have input_dim of 2, 1, and 4, respectively, then Xnew must have 7 columns and a covariance between the prediction points

cov_func(Xnew) = cov_func1(Xnew[:, :2]) * cov_func1(Xnew[:, 2:3]) * cov_func1(Xnew[:, 3:])

The distribution returned by conditional does not have a Kronecker structure regardless of whether the input points lie on a full grid. Therefore, Xnew does not need to have grid structure.

Parameters:

namestr

Name of the random variable

Xnewarray_like

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

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.