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dc.contributor.authorKrnjajic, Milovan
dc.identifier.citationDraper, David (2011) Bayesian Model Specification: Some problems related to model choice and calibration Proceedings of AMSA 2011, Novosibirsk, Russiaen_US
dc.description.abstractIn the development of Bayesian model specification for inference and prediction we focus on the conditional distributions p([theta],[beta]) and p(D[theta],[beta]), with data D and background assumptions [beta], and consider calibration (an assessment of how often we get the right answers) as an important integral step of the model development. We compare several predictive model-choice criteria and present related calibration results. In particular, we have implemented a simulation study to compare predictive model-choice criteria LS[cv] , a log-score based on cross-validation, LS[fs], a full-sample log score, with deviance information criterion, DIC. We show that for several classes of models DIC and LS[cv] are (strongly) negatively correlated; that LS[fs] has better small-sample model discrimination performance than either DIC, or LS[cv] ; we further demonstrate that when validating the model-choice results, a standard use of posterior predictive tail-area for hypothesis testing can be poorly calibrated and present a method for its proper calibration.en_US
dc.relation.ispartofProceedings of AMSA 2011, Novosibirsk, Russiaen
dc.subjectDeviance information criterionen_US
dc.subjectPosterior predictive tail areasen_US
dc.subjectHypothesis testingen_US
dc.titleBayesian Model Specification: Some problems related to model choice and calibrationen_US
dc.typeConference Paperen_US
dc.local.contactMilovan Krnjajic, School Of Mathematics Statistics, Room C205, Aras De Brun, Nui Galway. 2327 Email:

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