A General Metric for Riemannian Manifold Hamiltonian Monte Carlo

Markov Chain Monte Carlo (MCMC) is an invaluable means of inference with complicated models, and Hamiltonian Monte Carlo, in particular Riemannian Manifold Hamiltonian Monte Carlo (RMHMC), has demonstrated impressive success in many challenging problems. Current RMHMC implementations, however, rely...

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Bibliographic Details
Published in:arXiv.org 2013-09
Main Author: Betancourt, M J
Format: Article
Language:English
Subjects:
Computer simulation
Markov analysis
Markov chains
Monte Carlo simulation
Riemann manifold
Online Access:Get full text
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Summary:Markov Chain Monte Carlo (MCMC) is an invaluable means of inference with complicated models, and Hamiltonian Monte Carlo, in particular Riemannian Manifold Hamiltonian Monte Carlo (RMHMC), has demonstrated impressive success in many challenging problems. Current RMHMC implementations, however, rely on a Riemannian metric that limits their application to analytically-convenient models. In this paper I propose a new metric for RMHMC without these limitations and verify its success on a distribution that emulates many hierarchical and latent models.
ISSN:2331-8422
DOI:10.48550/arxiv.1212.4693