pymc.gp.MarginalApprox.marginal_likelihood#
- 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.
- Parameters
- 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.
- **kwargs
Extra keyword arguments that are passed to MvNormal distribution constructor.