Posterior Ramifications of Prior Dependence Structures

Abstract

Prior elicitation methods for Bayesian analyses transfigure prior information into quantifiable prior distributions. Recently, methods that leverage copulas have been proposed to accommodate more flexible dependence structures when eliciting multivariate priors. We show that the posterior cannot retain many of these flexible prior dependence structures in large-sample settings, and we emphasize that it is our responsibility as statisticians to communicate this to practitioners. We therefore overview objectives for prior specification that guide conversations between statisticians and practitioners to promote alignment between the flexibility in the prior dependence structure and the objectives for posterior analysis. Because correctly specifying the dependence structure a priori can be difficult, we consider how the choice of prior copula impacts the posterior distribution in terms of asymptotic convergence of the posterior mode. Our resulting recommendations clarify when it is useful to elicit intricate prior dependence structures and when it is not.