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dc.contributor.authorvan de Schoot, Rens
dc.contributor.authorDepaoli, Sarah
dc.contributor.authorKing, Ruth
dc.contributor.authorKramer, Bianca
dc.contributor.authorMärtens, Kaspar
dc.contributor.authorTadesse, Mahlet G.
dc.contributor.authorVannucci, Marina
dc.contributor.authorGelman, Andrew
dc.contributor.authorVeen, Duco
dc.contributor.authorWillemsen, Joukje
dc.contributor.authorYau, Christopher
dc.identifier.citationvan de Schoot, R., Depaoli, S., King, R., et al (2021) Bayesian statistics and modelling. Nature Reviews Methods Primers 1:1, 26pp. DOI:
dc.description.abstractBayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. This Primer describes the stages involved in Bayesian analysis, from specifying the prior and data models to deriving inference, model checking and refinement. We discuss the importance of prior and posterior predictive checking, selecting a proper technique for sampling from a posterior distribution, variational inference and variable selection. Examples of successful applications of Bayesian analysis across various research fields are provided, including in social sciences, ecology, genetics, medicine and more. We propose strategies for reproducibility and reporting standards, outlining an updated WAMBS (when to Worry and how to Avoid the Misuse of Bayesian Statistics) checklist. Finally, we outline the impact of Bayesian analysis on artificial intelligence, a major goal in the next decade.en_US
dc.rightsAttribution 4.0 International*
dc.titleBayesian statistics and modelling.en_US
dc.typeJournal Contributionen_US
dc.identifier.doi s43586-020-00001-2
dc.subject.dmProcessesData analysisen_US
dc.bibliographicCitation.titleNature Reviews Methods Primersen_US
dc.bibliographicCitation.issueArticle 1en_US
dc.description.adoptionValidated (tested by third parties)en_US
dc.description.methodologyTypeReports with methodological relevanceen_US van de Schoot

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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International