Title: MultiBUGS: A parallel implementation of the BUGS modelling framework for faster Bayesian inference,
Abstract: MultiBUGS (this https URL) is a new version of the general-purpose
Bayesian modelling software BUGS that implements a generic algorithm for
parallelising Markov chain Monte Carlo (MCMC) algorithms to speed up posterior
inference of Bayesian models. The algorithm parallelises evaluation of the
product-form likelihoods formed when a parameter has many children in the
directed acyclic graph (DAG) representation; and parallelises sampling of
conditionally-independent sets of parameters. A heuristic algorithm is used to
decide which approach to use for each parameter and to apportion computation
across computational cores. This enables MultiBUGS to automatically parallelise
the broad range of statistical models that can be fitted using BUGS-language
software, making the dramatic speed-ups of modern multi-core computing
accessible to applied statisticians, without requiring any experience of
parallel programming. We demonstrate the use of MultiBUGS on simulated data
designed to mimic a hierarchical e-health linked-data study of methadone
prescriptions including 425,112 observations and 20,426 random effects.
Posterior inference for the e-health model takes several hours in existing
software, but MultiBUGS can perform inference in only 28 minutes using 48
computational cores.