pymc.gp.MarginalApprox.conditional#
- MarginalApprox.conditional(name, Xnew, pred_noise=False, given=None, jitter=1e-06, **kwargs)[source]#
Return the approximate conditional distribution of the GP evaluated over new input locations Xnew.
- 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, Xu, 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
MvNormal
distribution constructor.
- name