pymc.gp.MarginalApprox.marginal_likelihood

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

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

Parameters:

namestr

Name of the random variable

Xarray_like

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

Xuarray_like

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

yarray_like

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

sigmafloat, Variable

Standard deviation of the Gaussian noise.

noisefloat, Variable, optional

Previous parameterization of sigma.

jitterfloat, 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.