B1069
Title: Robust Bayesian inference via coarsening
Authors: Jeff Miller - Harvard University (United States) [presenting]
David Dunson - Duke University (United States)
Abstract: The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, for large and/or high-dimensional datasets, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a simple, coherent approach to Bayesian inference that improves robustness to perturbations of the model: rather than condition on the data exactly, one conditions on a neighborhood of the empirical distribution. In certain cases, inference is easily implemented using standard methods. We illustrate with real and simulated data, and provide theoretical results.
