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dc.contributor.authorSimpson, Daniel
dc.contributor.authorRue, Håvard
dc.contributor.authorRiebler, Andrea Ingeborg
dc.contributor.authorMartins, Thiago Guerrera
dc.contributor.authorSørbye, Sigrunn Holbek
dc.date.accessioned2018-07-25T10:56:25Z
dc.date.available2018-07-25T10:56:25Z
dc.date.issued2017-04-06
dc.description.abstractIn this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base model. Proper priors are defined to penalise the complexity induced by deviating from the simpler base model and are formulated after the input of a user-defined scaling parameter for that model component, both in the univariate and the multivariate case. These priors are invariant to reparameterisations, have a natural connection to Jeffreys' priors, are designed to support Occam's razor and seem to have excellent robustness properties, all which are highly desirable and allow us to use this approach to define default prior distributions. Through examples and theoretical results, we demonstrate the appropriateness of this approach and how it can be applied in various situations.en_US
dc.descriptionAccepted manuscript version. Published version available at <a href=https://doi.org/10.1214/16-STS576> https://doi.org/10.1214/16-STS576</a>.en_US
dc.identifier.citationSimpson, D., Rue, H., Riebler. A., Martins, T.G. & Sørbye, S.H. (2017). Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors. Statistical Science, 32(1), 1-28. https://doi.org/10.1214/16-STS576en_US
dc.identifier.cristinIDFRIDAID 1464342
dc.identifier.doi10.1214/16-STS576
dc.identifier.issn0883-4237
dc.identifier.issn2168-8745
dc.identifier.urihttps://hdl.handle.net/10037/13263
dc.language.isoengen_US
dc.publisherInstitute of Mathematical Statistics (IMS)en_US
dc.relation.journalStatistical Science
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/FRINATEK/240873/Norway/Penalised Complexity-priors: A new tool to define default priors and robustify Bayesian models//en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410en_US
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410en_US
dc.subjectBayesian theoryen_US
dc.subjectinterpretable prior distributionsen_US
dc.subjecthierarchical modelsen_US
dc.subjectdisease mappingen_US
dc.subjectinformation geometryen_US
dc.subjectprior on correlation matricesen_US
dc.titlePenalising Model Component Complexity: A Principled, Practical Approach to Constructing Priorsen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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