SwISS: A scalable Markov chain Monte Carlo divide‐and‐conquer strategy

Abstract

Divide-and-conquer strategies for Monte Carlo algorithms are an increasingly popular approach to making Bayesian inference scalable to large data sets. In its simplest form, the data are partitioned across multiple computing cores and a separate Markov chain Monte Carlo algorithm on each core targets the associated partial posterior distribution, which we refer to as a sub-posterior, that is the posterior given only the data from the segment of the partition associated with that core. Divide-and-conquer techniques reduce computational, memory and disk bottle necks, but make it difficult to recombine the sub-posterior samples.We propose SwISS: Sub-posteriors with Inflation, Scaling and Shifting; a new approach for recombining the sub-posterior samples which is simple to apply, scales to high-dimensional parameter spaces and accurately approximates the original posterior distribution through affine transformations of the sub-posterior samples. We prove that our transformation is asymptotically optimal across a natural set of affine transformations and illustrate the efficacy of SwISS against competing algorithms on synthetic and real-world data sets.

A Bayesian identification framework for stochastic nonlinear dynamic systems based on a new likelihood approximation

“…• Scalable MCMC: Researchers have been working on developing scalable MCMC algorithms that can handle high-dimensional parameter spaces and large datasets. This includes using parallel and distributed computing techniques to speed up the sampling process [25].…”