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  • Time: Wednesday 10/25/2023 from 11:30 AM to 12:20 PM
  • Location: BLOC 448
  • Pizza and drinks provided

Topic

Bayes Factor Functions

Abstract

We provide closed-form expressions for Bayes factors based on \emph{z}, \emph{t}, $\chi^2$, and \emph{F} statistics. We define these Bayes factors as functions of the prior distributions used to define alternative hypotheses. The prior distributions represent non-local alternative prior densities centered on specified standardized effect sizes. The prior densities include a dispersion parameter that models the variation of effect sizes across replicated experiments. We examine the convergence rates of resulting Bayes factor functions under true null and true alternative hypotheses. Several examples illustrate the application of the resulting Bayes factor functions to replicated experimental designs. Finally, we compare the conclusions from these analyses to other default Bayes factor methods.

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