Applying diffusion-based Markov chain Monte Carlo

Radu Herbei, Rajib Paul and L Mark Berliner

PLOS ONE, 2017, vol. 12, issue 3, 1-14

Abstract: 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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0173453

DOI: 10.1371/journal.pone.0173453

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