def log_ml_estimate(self, proposal, n=1):
        
        prior = self.get_gp_prior()
        
        # sample from proposal
        samples = proposal.sample(n).samples
        
        # compute log likelihoods of samples
        log_likelihood=self.log_likelihood_multiple(samples)
        log_prior = prior.log_pdf(samples)
        log_proposal = proposal.log_pdf(samples)
        
        # compute estimate of marginal likelihood, log sum exp trick
        X=log_likelihood+log_prior-log_proposal
        
        return GPTools.log_mean_exp(X)
Save the model, and update caches.
dataset/ETHPy150Open karlnapf/kameleon-mcmc/kameleon_mcmc/gp/GaussianProcess.py/GaussianProcess.log_ml_estimate--crossover with--dataset/ETHPy150Open edoburu/django-fluent-contents/example/simplecms/models.py/Page.save