Applying diffusion-based Markov chain Monte Carlo

作者：Herbei Radu^*; Paul Rajib; Berliner L Mark

来源：PLos One, 2017, 12(3): e0173453.

DOI：10.1371/journal.pone.0173453

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.

• 出版日期2017-3-16