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International Conference on Geometric Science of Information

GSI 2013: Geometric Science of Information pp 327–334Cite as

A General Metric for Riemannian Manifold Hamiltonian Monte Carlo

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 8085)

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.

Keywords

  • Riemannian Manifold
  • Markov Chain Monte Carlo
  • Target Distribution
  • Hamiltonian Evolution
  • Euclidean Manifold

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-642-40020-9_35
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Author information

Authors and Affiliations

  1. Department of Statistics, Columbia University, New York, NY, 10027, USA

    Michael Betancourt

Authors
  1. Michael Betancourt
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Editor information

Editors and Affiliations

  1. Sony Computer Science Laboratories Inc, Tokyo, Japan

    Frank Nielsen

  2. Thales Land Air Systems, 91470, Limours, France

    Frédéric Barbaresco

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Betancourt, M. (2013). A General Metric for Riemannian Manifold Hamiltonian Monte Carlo. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2013. Lecture Notes in Computer Science, vol 8085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40020-9_35

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  • DOI: https://doi.org/10.1007/978-3-642-40020-9_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40019-3

  • Online ISBN: 978-3-642-40020-9

  • eBook Packages: Computer ScienceComputer Science (R0)