pymc.gp.MarginalKron.marginal_likelihood#
- MarginalKron.marginal_likelihood(name, Xs, y, sigma, is_observed=True, **kwargs)[source]#
Returns the marginal likelihood distribution, given the input locations cartesian(*Xs) and the data y.
- Parameters
- name: string
Name of the random variable
- Xs: list of array-like
Function input values for each covariance function. Each entry must be passable to its respective covariance without error. The total covariance function is measured on the full grid cartesian(*Xs).
- 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 white Gaussian noise.
- is_observed: bool
Whether to set y as an observed variable in the model. Default is True.
- **kwargs
Extra keyword arguments that are passed to KroneckerNormal distribution constructor.