- MarginalApprox.predict(Xnew, point=None, diag=False, pred_noise=False, given=None, jitter=1e-06, model=None)#
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.
- Xnew: array-like
Function input values. If one-dimensional, must be a column vector with shape (n, 1).
- point: pymc.model.Point
A specific point to condition on.
- diag: bool
If True, return the diagonal instead of the full covariance matrix. Default is False.
- pred_noise: bool
Whether or not observation noise is included in the conditional. Default is False.
- given: dict
Same as conditional method.
- jitter: scalar
A small correction added to the diagonal of positive semi-definite covariance matrices to ensure numerical stability.