pymc.gp.Marginal.marginal_likelihood#
- Marginal.marginal_likelihood(name, X, y, sigma=None, noise=None, jitter=1e-06, is_observed=True, **kwargs)[source]#
Returns the marginal likelihood distribution, given the input locations X and the data y.
This is integral over the product of the GP prior and a normal likelihood.
\[y \mid X,\theta \sim \int p(y \mid f,\, X,\, \theta) \, p(f \mid X,\, \theta) \, df\]- 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).
- 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, or Covariance
Standard deviation of the Gaussian noise. Can also be a Covariance for non-white noise.
- noise: scalar, Variable, or Covariance
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.