def log_marginal_likelihood(params, data):
cluster_lls = []
for log_proportion, mean, chol in zip(*unpack_params(params)):
cov = np.dot(chol.T, chol) + 0.000001 * np.eye(D)
cluster_log_likelihood = log_proportion + mvn.logpdf(data, mean, cov)
cluster_lls.append(np.expand_dims(cluster_log_likelihood, axis=0))
cluster_lls = np.concatenate(cluster_lls, axis=0)
return np.sum(logsumexp(cluster_lls, axis=0))
def log_marginal_likelihood(params, x, y):
mean, cov_params, noise_scale = unpack_params(params)
cov_y_y = cov_func(cov_params, x, x) + noise_scale * np.eye(len(y))
prior_mean = mean * np.ones(len(y))
return mvn.logpdf(y, prior_mean, cov_y_y)
