MarginalKron.conditional(name, Xnew, pred_noise=False, diag=False, **kwargs)[source]#

Returns the conditional distribution evaluated over new input locations Xnew, just as in Marginal.

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.


Name of the random variable


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.


Extra keyword arguments that are passed to MvNormal distribution constructor.