Whenever a set of models is applied to data, uncertainty surrounds both the selection of the best model and the estimation of the model parameters. The coherent Bayesian answer to the model selection question is to avoid all-or-none selection altogether and instead retain model uncertainty, employing it for purposes such as prediction and parameter estimation. Advantages of this multi-model approach include a reduction of overconfidence, improved predictive performance, and an increased robustness against model misspecification. Moreover, the multi-model framework can be seamlessly integrated with the recent open-science ideals of multi-team inference and multiverse analyses. We debunk several philosophical objections to Bayesian multi-model inference and demonstrate its practical use for problems ranging from the simple to the complex.
ABOUT THE SPEAKER
Eric-Jan Wagenmakers is a mathematical psychologist and a dedicated Bayesian. He works for the Psychological Methods unit at the University of Amsterdam where he coordinates the team that develops JASP, an open-source software program for statistical analyses (www.jasp-stats.org). Wagenmakers is also a strong advocate of open science and the preregistration of analysis plans. For more information see ejwagenmakers.com