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

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|October 19, 2020
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Summary
This summary is machine-generated.

MultiBUGS accelerates Bayesian statistical modeling by parallelizing Markov chain Monte Carlo (MCMC) algorithms on multi-core processors. This new software significantly reduces computation time for complex models, making advanced Bayesian inference more accessible.

Keywords:
BUGSBayesian analysisGibbs samplingMarkov chain Monte Carlodirected acyclic graphhierarchical modelsparallel computing

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Area of Science:

  • Computational Statistics
  • Bayesian Inference
  • Software Development

Background:

  • Bayesian modeling software like BUGS is widely used but can be computationally intensive.
  • Markov chain Monte Carlo (MCMC) algorithms are standard for posterior inference but often slow.
  • Modern multi-core processors offer significant computational power that is not fully utilized by existing Bayesian software.

Purpose of the Study:

  • To introduce MultiBUGS, a novel version of BUGS software designed to accelerate Bayesian inference.
  • To implement a generic parallelization algorithm for MCMC methods within the BUGS framework.
  • To enable applied statisticians to leverage multi-core computing for complex Bayesian models without parallel programming expertise.

Main Methods:

  • Developed a generic algorithm to parallelize MCMC computations for Bayesian models.
  • Implemented parallelization strategies for product-form likelihoods and conditionally-independent parameter sets.
  • Utilized a heuristic algorithm to optimize computation distribution across cores.
  • Applied MultiBUGS to a simulated hierarchical e-health linked-data study.

Main Results:

  • MultiBUGS successfully parallelizes a broad range of BUGS-compatible statistical models.
  • Demonstrated significant speed-up on a large-scale simulated e-health dataset (425,112 observations, 20,426 random effects).
  • Reduced posterior inference time from several hours to 28 minutes using 48 cores for the e-health model.

Conclusions:

  • MultiBUGS effectively accelerates Bayesian posterior inference through automatic MCMC parallelization.
  • The software makes advanced computational techniques accessible to applied statisticians.
  • MultiBUGS represents a significant advancement in computational Bayesian statistics, enhancing efficiency for complex data analyses.