def get_log_upper_proba_distribution_gp(gaussian_process: GaussianProcess,
theta: np.ndarray):
"""
This functions evaluates log( p_1(theta | X, y) ) where:
- p_1 = Z * p
- p is the posterior distribution
- p_1 is easy to calculate
There are 2 methods that you might find useful in the class GaussianProcess:
- get_log_marginal_likelihood
- get_log_prior_at
:param gaussian_process
:param theta: parameters at which we evaluate p_1. In our example, it is a numpy array (row vector)
of shape (6,). As our linear + gaussian kernel depends on 6 real numbers.
:return: log( p_1(theta | X, y) )
"""
# TODO
log_marginal_likelihood = gaussian_process.get_log_marginal_likelihood(
*theta)
log_prior = gaussian_process.get_log_prior_at(*theta)
return log_marginal_likelihood + log_prior
