Basics
Tutorials
Models
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) |
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prior_transform(x) |
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run_nested(gp[, metric]) |
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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) |