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MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference

Goudie, RJB; Turner, RM; De Angelis, D; Thomas, A; (2020) MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference. Journal of Statistical Software , 95 (7) 10.18637/jss.v095.i07. Green open access

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Abstract

MultiBUGS is a new version of the general-purpose Bayesian modeling software BUGS that implements a generic algorithm for parallelizing Markov chain Monte Carlo (MCMC) algorithms to speed up posterior inference of Bayesian models. The algorithm parallelizes evaluation of the product-form likelihoods formed when a parameter has many children in the directed acyclic graph (DAG) representation; and parallelizes 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 parallelize 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

Type: Article
Title: MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference
Open access status: An open access version is available from UCL Discovery
DOI: 10.18637/jss.v095.i07
Publisher version: https://doi.org/10.18637/jss.v095.i07
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 3.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/
Keywords: BUGS, parallel computing, Markov chain Monte Carlo, Gibbs sampling, Bayesian analysis, hierarchical models, directed acyclic graph.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10114538
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