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dc.contributor.authorBuchholz, Alexander
dc.contributor.authorAhfock, Daniel
dc.contributor.authorRichardson, Sylvia
dc.description.abstractWe propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets. These subsets are only accessed locally by individual workers and no data is shared between the workers. We approximate the model evidence for the full data set through Monte Carlo sampling from the posterior on every subset generating a model evidence per subset. The results are combined using a novel approach which corrects for the splitting using summary statistics of the generated samples. Our divide-and-conquer approach enables Bayesian model choice in the large data setting, exploiting all available information but limiting communication between workers. We derive theoretical error bounds that quantify the resulting trade-off between computational gain and loss in precision. The embarrassingly parallel nature yields important speed-ups when used on massive data sets as illustrated by our real world experiments. In addition, we show how the suggested approach can be extended to model choice within a reversible jump setting that explores multiple feature combinations within one run.
dc.description.sponsorshipEPSRC (EP/R018561/1) and the Alan Turing Institute (TU/B/00092)
dc.publisherInstitute of Mathematical Statistics
dc.rightsAttribution 4.0 International
dc.titleDistributed Computation for Marginal Likelihood based Model Choice
dc.publisher.departmentMrc Biostatistics Unit
prism.publicationNameBayesian Analysis
rioxxterms.typeJournal Article/Review
cam.orpheus.success2022-10-12: VoR added to Apollo record
pubs.licence-display-nameApollo Repository Deposit Licence Agreement

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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International