The R2D2 prior for generalized linear mixed models.

ABSTRACT: In Bayesian analysis, the selection of a prior distribution is typically done by considering each parameter in the model. While this can be convenient, in many scenarios it may be desirable to place a prior on a summary measure of the model instead. In this work, we propose a prior on the model fit, as measured by a Bayesian coefficient of determination ( R2 ), which then induces a prior on the individual parameters. We achieve this by placing a beta prior on R2 and then deriving the induced prior on the global variance parameter for generalized linear mixed models. We derive closed-form expressions in many scenarios and present several approximation strategies when an analytic form is not possible and/or to allow for easier computation. In these situations, we suggest approximating the pr

SUBMITTER: Yanchenko E 

PROVIDER: S-EPMC12007809 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature