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JSIAM Letters
Vol. 9 (2017) p. 33-36




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.

Copyright © 2015, The Japan Society for Industrial and Applied Mathematics

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