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arXiv · 1905.11916

Selecting the Metric in Hamiltonian Monte Carlo

Ben Bales·Arya Pourzanjani·Aki Vehtari·Linda Petzold
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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.

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Ben Bales, Arya Pourzanjani, Aki Vehtari, Linda Petzold. 2019-05-28. Selecting the Metric in Hamiltonian Monte Carlo. https://arxiv.org/abs/1905.11916

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