Hamiltonian Monte Carlo with explicit, reversible, and volume-preserving adaptive step size control

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  • Okudo Michiko
    Graduate School of Information Science and Technology, the University of Tokyo
  • Suzuki Hideyuki
    Graduate School of Information Science and Technology, Osaka University

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<p> Hamiltonian Monte Carlo is a Markov chain Monte Carlo method that uses Hamiltonian dynamics to efficiently produce distant samples. It employs geometric numerical integration to simulate Hamiltonian dynamics, which is a key of its high performance. We present a Hamiltonian Monte Carlo method with adaptive step size control to further enhance the efficiency. We propose a new explicit, reversible, and volume-preserving integration method to adaptively set the step sizes, which does not violate the detailed balance condition or require a large increase in computational time. </p>

収録刊行物

  • JSIAM Letters

    JSIAM Letters 9 (0), 33-36, 2017

    一般社団法人 日本応用数理学会

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