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dc.contributor.authorHuggins, Jonathan H.en_US
dc.contributor.authorMiller, Jeffrey W.en_US
dc.date.accessioned2021-04-07T18:49:46Z
dc.date.available2021-04-07T18:49:46Z
dc.date.issued2020-07
dc.identifier.citationJonathan H Huggins, Jeffrey W Miller. 2020. "Robust and Reproducible Model Selection Using Bagged Posteriors." arXiv.org, Volume arXiv:2007.14845 [stat.ME], https://arxiv.org/abs/2007.14845.
dc.identifier.urihttps://hdl.handle.net/2144/42365
dc.description.abstractBayesian model selection is premised on the assumption that the data are generated from one of the postulated models, however, in many applications, all of these models are incorrect. When two or more models provide a nearly equally good t to the data, Bayesian model selection can be highly unstable, potentially leading to self-contradictory ndings. In this paper, we explore using bagging on the posterior distribution (\BayesBag") when performing model selection { that is, averaging the posterior model probabilities over many bootstrapped datasets. We provide theoreti- cal results characterizing the asymptotic behavior of the standard posterior and the BayesBag posterior under misspeci cation, in the model selection setting. We empir- ically assess the BayesBag approach on synthetic and real-world data in (i) feature selection for linear regression and (ii) phylogenetic tree reconstruction. Our theory and experiments show that in the presence of misspeci cation, BayesBag provides (a) greater reproducibility and (b) greater accuracy in selecting the correct model, compared to the standard Bayesian posterior; on the other hand, under correct speci- cation, BayesBag is slightly more conservative than the standard posterior. Overall, our results demonstrate that BayesBag provides an easy-to-use and widely applicable approach that improves upon standard Bayesian model selection by making it more stable and reproducible.en_US
dc.language.isoen_US
dc.relation.ispartofarXiv.org
dc.subjectAsymptoticsen_US
dc.subjectBaggingen_US
dc.subjectBayesian model averagingen_US
dc.subjectBootstrapen_US
dc.subjectModel misspecificationen_US
dc.subjectStabilityen_US
dc.titleRobust and reproducible model selection using bagged posteriorsen_US
dc.typeArticleen_US
dc.description.versionFirst author draften_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Mathematics & Statisticsen_US
pubs.publication-statusSubmitteden_US
dc.identifier.mycv592287


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