Open AccessApplying diffusion-based Markov chain Monte Carlo
Author(s) -
Radu Herbei,
Rajib Paul,
L. Mark Berliner
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0173453
Subject(s) - markov chain monte carlo , computer science , bayesian probability , metropolis–hastings algorithm , monte carlo method , context (archaeology) , prior probability , markov chain , algorithm , mathematical optimization , hybrid monte carlo , mathematics , statistical physics , artificial intelligence , machine learning , statistics , physics , paleontology , biology
We examine the performance of a strategy for Markov chain Monte Carlo (MCMC) developed by simulating a discrete approximation to a stochastic differential equation (SDE). We refer to the approach as diffusion MCMC . A variety of motivations for the approach are reviewed in the context of Bayesian analysis. In particular, implementation of diffusion MCMC is very simple to set-up, even in the presence of nonlinear models and non-conjugate priors. Also, it requires comparatively little problem-specific tuning. We implement the algorithm and assess its performance for both a test case and a glaciological application. Our results demonstrate that in some settings, diffusion MCMC is a faster alternative to a general Metropolis-Hastings algorithm.