AbstractPrior

class gpjax.gps.AbstractPrior(kernel, mean_function, jitter=1e-06)

Bases: _SummaryMixin, Module, Generic[M, K]

Abstract Gaussian process prior.

Parameters:

• kernel (K)

• mean_function (M)

• jitter (float)

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

Evaluate the predictive distribution.

Compute the latent function’s multivariate normal distribution for a given set of parameters. For any class inheriting the AbstractPrior 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.

• 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