heron.training

These are functions designed to be used for training a Gaussian process made using heron.

Functions

cross_validation(p, gp)	Calculate the cross-validation factor between the training set and the test set.
ln_likelihood(p, gp)	Returns to log-likelihood of the Gaussian process, which can be used to learn the hyperparameters of the GP.
logp(x)
prior_transform(x)
run_nested(gp[, metric])
run_sampler(sampler, initial, iterations)	Run the MCMC sampler for some number of iterations, but output a progress bar so you can keep track of what’s going on
run_training_map(gp[, metric, repeats])	Find the maximum a posteriori training values for the Gaussian Process.
run_training_mcmc(gp[, walkers, burn, …])	Train a Gaussian process using an MCMC process to find the maximum evidence.
run_training_nested(gp[, method, maxiter, …])	Train the Gaussian Process model using nested sampling.
train_cv(gp)