GP.logL_hessianable_stored#
- GP.logL_hessianable_stored(p: Any, Y: Array, stored_values: Any) Tuple[Any, Any] [source]#
Computes the log likelihood and also returns any stored values from the decomposition of the covariance matrix. This function is slower for gradient calculations than
GP.logL_stored
but is more numerically stable for second-order derivative calculations as required when calculating the hessian. This function still only returns the log likelihood so jax.hessian must be applied to return the hessian of the log likelihood.- 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.
Y (JAXArray) – Observed data to fit, must be of shape
(N_l, N_t)
.stored_values (PyTree) – Stored values from the decomposition of the covariance matrix. The specific values contained in this PyTree depend on the choice of
Kernel
object and are returned byKernel.decomp_fn
.
- Returns:
A tuple where the first element is the value of the log likelihood. The second element is a PyTree which contains stored values from the decomposition of the covariance matrix.
- Return type:
(Scalar, PyTree)