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2021 Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo
Alexander Buchholz, Nicolas Chopin, Pierre E. Jacob
Bayesian Anal. Advance Publication 1-27 (2021). DOI: 10.1214/20-BA1222

Abstract

Sequential Monte Carlo (SMC) samplers are an alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to rejuvenate particles. We discuss how to calibrate automatically (using the current particles) Hamiltonian Monte Carlo kernels within SMC. To do so, we build upon the adaptive SMC approach of Fearnhead and Taylor (2013), and we also suggest alternative methods. We illustrate the advantages of using HMC kernels within an SMC sampler via an extensive numerical study.

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Alexander Buchholz. Nicolas Chopin. Pierre E. Jacob. "Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo." Bayesian Anal. Advance Publication 1 - 27, 2021. https://doi.org/10.1214/20-BA1222

Information

Published: 2021
First available in Project Euclid: 31 July 2020

Digital Object Identifier: 10.1214/20-BA1222

Subjects:
Primary: 62F15
Secondary: 65C05

JOURNAL ARTICLE
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