pymc.gp.Marginal.conditional#
- Marginal.conditional(name, Xnew, pred_noise=False, 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:
\[f_* \mid f, X, X_* \sim \mathcal{GP}\left( K(X_*, X) [K(X, X) + K_{n}(X, X)]^{-1} f \,, K(X_*, X_*) - K(X_*, X) [K(X, X) + K_{n}(X, X)]^{-1} K(X, X_*) \right)\]- 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).
- 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.
- **kwargs
Extra keyword arguments that are passed to
MvNormaldistribution constructor.
- name