Induction and Deduction in Bayesian Data Analysis

dc.contributor.authorGelman, Andrew
dc.date.accessioned2021-12-08T20:51:32Z
dc.date.available2021-12-08T20:51:32Z
dc.date.issued2011
dc.description.abstractThe classical or frequentist approach to statistics (in which inference is centered on significance testing), is associated with a philosophy in which science is deductive and follows Popper's doctrine of falsification. In contrast, Bayesian inference is commonly associated with inductive reasoning and the idea that a model can be dethroned by a competing model but can never be directly falsified by a significance test. The purpose of this article is to break these associations, which I think are incorrect and have been detrimental to statistical practice, in that they have steered falsificationists away from the very useful tools of Bayesian inference and have discouraged Bayesians from checking the fit of their models. From my experience using and developing Bayesian methods in social and environmental science, I have found model checking and falsification to be central in the modeling process.de_DE
dc.description.sponsorshipNational Science Foundation (NSF); ROR-ID:021nxhr62de_DE
dc.identifier.urihttps://jlupub.ub.uni-giessen.de//handle/jlupub/441
dc.identifier.urihttp://dx.doi.org/10.22029/jlupub-374
dc.language.isoende_DE
dc.subject.ddcddc:100de_DE
dc.subject.ddcddc:330de_DE
dc.titleInduction and Deduction in Bayesian Data Analysisde_DE
dc.typearticlede_DE
dcterms.isPartOf2536124-7
local.affiliationExterne Einrichtungende_DE
local.source.epage78de_DE
local.source.journaltitleRationality, markets, and morals: RMMde_DE
local.source.spage67de_DE
local.source.volume2de_DE

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