pymc.gp.MarginalApprox.conditional#
- MarginalApprox.conditional(name, Xnew, pred_noise=False, given=None, jitter=1e-06, **kwargs)[source]#
Returns the approximate conditional distribution of the GP evaluated over new input locations Xnew.
- Parameters
- name: string
Name of the random variable
- Xnew: array-like
Function input values. If one-dimensional, must be a column vector with shape (n, 1).
- pred_noise: bool
Whether or not observation noise is included in the conditional. Default is False.
- given: dict
Can optionally take as key value pairs: X, Xu, y, sigma, and gp. See the section in the documentation on additive GP models in PyMC for more information.
- jitter: scalar
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 MvNormal distribution constructor.