pymc.gp.Latent.conditional#
- Latent.conditional(name, Xnew, given=None, jitter=1e-06, **kwargs)[source]#
Returns the conditional distribution evaluated over new input locations Xnew.
Given a set of function values f that the GP prior was over, the conditional distribution over a set of new points, f_* is
\[f_* \mid f, X, X_* \sim \mathcal{GP}\left( K(X_*, X) K(X, X)^{-1} f \,, K(X_*, X_*) - K(X_*, X) K(X, X)^{-1} K(X, X_*) \right)\]- Parameters:
- name
str Name of the random variable
- Xnewarray_like
Function input values. If one-dimensional, must be a column vector with shape (n, 1).
- given
dict, optional Can take as key value pairs: X, y, 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.
- **kwargs
Extra keyword arguments that are passed to
MvNormaldistribution constructor.
- name