GP.logP_hessianable

GP.logP_hessianable(p: Any, Y: Array) → Any

Computes the log posterior without returning any stored values from the decomposition of the covariance matrix. This function is slower for gradient calculations than GP.logP but is more numerically stable for second-order derivative calculations as required when calculating the hessian. This function still only returns the log posterior so jax.hessian must be applied to return the hessian of the log posterior.

Parameters:

• p (PyTree) – Pytree of hyperparameters used to calculate the covariance matrix in addition to any mean function parameters which may be needed to calculate the mean function. Also input to the logPrior function for the calculation of the log priors.

• Y (JAXArray) – Observed data to fit, must be of shape (N_l, N_t).

Returns:

The value of the log posterior.

Return type:

Scalar