AbstractPosterior

class gpjax.gps.AbstractPosterior(prior, likelihood, jitter=1e-06)

Bases: _SummaryMixin, Module, Generic[P, L]

Abstract Gaussian process posterior.

The base GP posterior object conditioned on an observed dataset. All posterior objects should inherit from this class.

Parameters:

• prior (AbstractPrior)

• likelihood (Any)

• jitter (float)

abstractmethod predict(test_inputs, train_data, *, return_covariance_type='dense')

Compute the latent function’s multivariate normal distribution for a given set of parameters. For any class inheriting the AbstractPosterior class, this method must be implemented.

Parameters:

• test_inputs (Num[jaxlib._jax.Array, 'N D'] | Num[ndarray, 'N D']) – Input locations where the GP should be evaluated.

• train_data (Dataset) – Training dataset to condition on.

• return_covariance_type (Literal['dense', 'diagonal']) – Literal denoting whether to return the full covariance of the joint predictive distribution at the test_inputs (dense) or just the the standard-deviation of the predictive distribution at the test_inputs.

Returns:

A multivariate normal random variable representation

of the Gaussian process.

Return type:

GaussianDistribution