MultiBUGS: A Parallel Implementation of the BUGS Modelling Framework for Faster Bayesian Inference
Authors
Goudie, RJB
Turner, Rebecca M
De Angelis, Daniella
Thomas, Andrew
Publication Date
2020-10-07Journal Title
Journal of Statistical Software
ISSN
1548-7660
Publisher
Foundation for Open Access Statistics
Volume
95
Issue
7
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Goudie, R., Turner, R. M., De Angelis, D., & Thomas, A. (2020). MultiBUGS: A Parallel Implementation of the BUGS Modelling Framework for Faster Bayesian Inference. Journal of Statistical Software, 95 (7) https://doi.org/10.18637/jss.v095.i07
Abstract
MultiBUGS 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.
Keywords
BUGS, Bayesian analysis, Gibbs sampling, Markov chain Monte Carlo, directed acyclic graph, hierarchical models, parallel computing
Sponsorship
MRC (unknown)
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.18637/jss.v095.i07
This record's URL: https://www.repository.cam.ac.uk/handle/1810/288297
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: support@repository.cam.ac.uk