Robust Probabilistic Inference via a Constrained Transport Metric

Flexible Bayesian models are typically constructed using limits of large parametric models with a multitude of parameters that are often difficult to interpret. In this article, we offer a novel alternative by constructing an exponentially tilted empirical likelihood carefully designed to concentrate near a parametric family of distributions of choice with respect to a novel variant of the Wasserstein metric, which is then combined with a prior distribution on model parameters to obtain a robustified posterior. The proposed approach finds applications in a wide variety of robust inference problems, where we intend to perform inference on the parameters associated with the centering distribution in the presence of outliers. Our proposed transport metric enjoys great computational simplicity and is inherently parallelizable, exploiting the Sinkhorn regularization for discrete optimal transport problems. We demonstrate superior performance of our methodology when compared against state-of-the-art robust Bayesian inference methods. We also demonstrate the equivalence of our approach with a non-parametric Bayesian formulation under a suitable asymptotic framework, thereby testifying to its flexibility.