Basics

Tutorials

Models

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)