Subjective Bayesian testing using calibrated prior probabilities Spitzner, Dan
dc.contributor.department Center for Statistics and Applications in Forensic Evidence 2020-04-10T01:12:08.000 2020-06-30T01:58:13Z 2020-06-30T01:58:13Z Tue Jan 01 00:00:00 UTC 2019 2019-01-01
dc.description.abstract <p>This article proposes a calibration scheme for Bayesian testing that coordinates analytically-derived statistical performance considerations with expert opinion. In other words, the scheme is effective and meaningful for incorporating <em>objective</em> elements into <em>subjective</em> Bayesian inference. It explores a novel role for default priors as anchors for calibration rather than substitutes for prior knowledge. Ideas are developed for use with multiplicity adjustments in multiple-model contexts, and to address the issue of prior sensitivity of Bayes factors. Along the way, the performance properties of an existing multiplicity adjustment related to the Poisson distribution are clarified theoretically. Connections of the overall calibration scheme to the Schwarz criterion are also explored. The proposed framework is examined and illustrated on a number of existing data sets related to problems in clinical trials, forensic pattern matching, and log-linear models methodology.</p>
dc.description.comments <p>This is a manuscript of an article published as Spitzner, Dan J. "Subjective Bayesian testing using calibrated prior probabilities." <em>Brazilian Journal of Probability and Statistics</em> 33, no. 4 (2019): 861-893. Posted with permission of CSAFE.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 1034
dc.identifier.contextkey 17326942
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath csafe_pubs/25
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 22:56:57 UTC 2022
dc.source.uri 10.1214/18-BJPS424
dc.subject.disciplines Forensic Science and Technology
dc.title Subjective Bayesian testing using calibrated prior probabilities
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication d8a3c72b-850f-40f6-87c4-8812547080c7
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