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Geometric Science of Information

Volume 8085 of the series Lecture Notes in Computer Science pp 327-334

A General Metric for Riemannian Manifold Hamiltonian Monte Carlo

  • Michael BetancourtAffiliated withDepartment of Statistics, Columbia University

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Abstract

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 success in many challenging problems. Current RMHMC implementations, however, rely on a Riemannian metric that limits their application. 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.