Exact GPs (Regression)¶
Regression with a Gaussian noise model is the canonical example of Gaussian processes. These examples will work for small to medium sized datasets (~2,000 data points). All examples here use exact GP inference.
Simple GP Regression is the basic tutorial for regression in GPyTorch.
Spectral Mixture Regression extends on the previous example with a more complex kernel.
Fully Bayesian GP Regression demonstrates how to perform fully Bayesian inference by sampling the GP hyperparameters using NUTS. (This example requires Pyro to be installed).
Distributional GP Regression is an example of how to take account of uncertainty in inputs.
Dirichlet Classification is an example of how to perform regression on classification labels via an approximate likelihood.