A1207
Title: Reproducible Bayesian model selection and high-dimensional regression
Authors: Jonathan Huggins - Boston University (United States) [presenting]
Abstract: If slightly changing a model specification or including more data results in contradictory inferences, then the validity of any conclusions drawn from such inferences is put in doubt: they are not, in a statistical sense, reproducible. Motivated by examples ranging from phylogenetic tree reconstruction to crime rate prediction, the aim is to discuss how model misspecification can result in standard Bayesian inference, leading to such non-reproducible results. An easy-to-implement solution, the bagged posterior, is also outlined.
