pymc.gp.MarginalApprox.predict#
- MarginalApprox.predict(Xnew, point=None, diag=False, pred_noise=False, given=None, jitter=1e-06, model=None)#
Return mean and covariance of the conditional distribution given a point.
The point might be 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.
- given
dict, 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.
- jitter
float, default 1e-6 A small correction added to the diagonal of positive semi-definite covariance matrices to ensure numerical stability.
- 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.