MarginalApprox.marginal_likelihood(name, X, Xu, y, sigma=None, noise=None, jitter=1e-06, **kwargs)[source]#

Returns the approximate marginal likelihood distribution, given the input locations X, inducing point locations Xu, data y, and white noise standard deviations sigma.

name: string

Name of the random variable

X: array-like

Function input values. If one-dimensional, must be a column vector with shape (n, 1).

Xu: array-like

The inducing points. Must have the same number of columns as X.

y: array-like

Data that is the sum of the function with the GP prior and Gaussian noise. Must have shape (n, ).

sigma: scalar, Variable

Standard deviation of the Gaussian noise.

noise: scalar, Variable

Previous parameterization of sigma

jitter: scalar

A small correction added to the diagonal of positive semi-definite covariance matrices to ensure numerical stability.


Extra keyword arguments that are passed to MvNormal distribution constructor.