Discussion Lead: Bob Carpenter [CCM]
Topic: Bales, Pourzanjani, Petzold. 2019. Selecting the metric in HMC.
Link: https://arxiv.org/abs/1905.11916
Abstract: 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.