Selecting the Metric in Hamiltonian Monte Carlo

Understand

We present a selection criterion for the Euclidean metric adapted during warmup in a Hamiltonian Monte Carlo sampler that makes it possible for a sampler to automatically pick the metric based on the model and the availability of warmup draws.

• Additionally, we present a new adaptation inspired by the selection criterion that requires significantly fewer warmup draws to be effective.
• The effectiveness of the selection criterion and adaptation are demonstrated on a number of applied problems.
• An implementation for the Stan probabilistic programming language is provided.