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dc.contributor.authorHammer, Hugo Lewi
dc.contributor.authorRiegler, Michael
dc.date.accessioned2023-11-06T12:04:29Z
dc.date.available2023-11-06T12:04:29Z
dc.date.issued2023-02-01
dc.description.abstractApproximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable. For existing variants of ABC and BSL, the focus is usually first put on the simulation algorithm, and after that the form of the resulting approximate posterior distribution comes as a consequence of the algorithm. In this paper we turn this around and firstly define a reasonable approximate posterior distribution by studying the distributional properties of the expected discrepancy, or more generally an expected evaluation, with respect to generated samples from the model. The resulting approximate posterior distribution will be on a simple and interpretable form compared to ABC and BSL. <p><p>Secondly a Markov chain Monte Carlo (MCMC) algorithm is developed to simulate from the resulting approximate posterior distribution. The algorithm was evaluated on a synthetic data example and on the Stepping Stone population genetics model, demonstrating that the proposed scheme has real world applicability. The algorithm demonstrates competitive results with the BSL and sequential Monte Carlo ABC algorithms, but is outperformed by the ABC MCMC.en_US
dc.identifier.citationHammer HL, Riegler M, Tjelmeland H. Approximate Bayesian Inference Based on Expected Evaluations. Bayesian Analysis. 2023en_US
dc.identifier.cristinIDFRIDAID 2191691
dc.identifier.issn1936-0975
dc.identifier.issn1931-6690
dc.identifier.urihttps://hdl.handle.net/10037/31678
dc.language.isoengen_US
dc.publisherInternational Society for Bayesian Analysisen_US
dc.relation.journalBayesian Analysis
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 International Society for Bayesian Analysisen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleApproximate Bayesian Inference Based on Expected Evaluationsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)