GP.logP#
- GP.logP(p: Any, Y: Array) Any[source]#
Computes the log posterior without returning any stored values from the decomposition of the covariance matrix.
- 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