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- W3163707242 abstract "In most applications of Bayesian model comparison or Bayesian hypothesis testing, the results are reported in terms of the Bayes factor only, not in terms of the posterior probabilities of the models. Posterior model probabilities are not reported because researchers are reluctant to declare prior model probabilities, which in turn stems from uncertainty in the prior. Fortunately, Bayesian formalisms are designed to embrace prior uncertainty, not ignore it. This article provides a novel derivation of the posterior distribution of model probability, and shows many examples. The posterior distribution is useful for making decisions taking into account the uncertainty of the posterior model probability. Benchmark Bayes factors are provided for a spectrum of priors on model probability. R code is posted at https://osf.io/36527/. This framework and tools will improve interpretation and usefulness of Bayes factors in all their applications." @default.
- W3163707242 created "2021-05-24" @default.
- W3163707242 creator A5087217252 @default.
- W3163707242 date "2021-04-18" @default.
- W3163707242 modified "2023-10-14" @default.
- W3163707242 title "Uncertainty of prior and posterior model probability: Implications for interpreting Bayes factors" @default.
- W3163707242 doi "https://doi.org/10.31219/osf.io/edh7j" @default.
- W3163707242 hasPublicationYear "2021" @default.
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