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. © 2013 Springer-Verlag.
CITATION STYLE
Betancourt, M. (2013). A general metric for Riemannian manifold Hamiltonian Monte Carlo. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8085 LNCS, pp. 327–334). https://doi.org/10.1007/978-3-642-40020-9_35
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