Reflection, refraction, and Hamiltonian Monte Carlo

Hadi Mohasel Afshar, Justin Domke

    Research output: Contribution to journalConference articlepeer-review

    31 Citations (Scopus)

    Abstract

    Hamiltonian Monte Carlo (HMC) is a successful approach for sampling from continuous densities. However, it has difficulty simulating Hamiltonian dynamics with non-smooth functions, leading to poor performance. This paper is motivated by the behavior of Hamiltonian dynamics in physical systems like optics. We introduce a modification of the Leapfrog discretization of Hamiltonian dynamics on piecewise continuous energies, where intersections of the trajectory with discontinuities are detected, and the momentum is reflected or refracted to compensate for the change in energy. We prove that this method preserves the correct stationary distribution when boundaries are affine. Experiments show that by reducing the number of rejected samples, this method improves on traditional HMC.

    Original languageEnglish
    Pages (from-to)3007-3015
    Number of pages9
    JournalAdvances in Neural Information Processing Systems
    Volume2015-January
    Publication statusPublished - 2015
    Event29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada
    Duration: 7 Dec 201512 Dec 2015

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