pymc.gp.MarginalKron.predict#
- MarginalKron.predict(Xnew, point=None, diag=False, pred_noise=False, model=None)[source]#
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:
- Xnewarray_like
Function input values. If one-dimensional, must be a column vector with shape (n, 1).
- point
pymc.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.
- model
Model, 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.