A Geometric View of Posterior Approximation

Abstract

Although Bayesian methods are robust and principled, their application in practice could be limited since they typically rely on computationally intensive Markov Chain Monte Carlo algorithms for their implementation. One possible solution is to find a fast approximation of posterior distribution and use it for statistical inference. For commonly used approximation methods, such as Laplace and variational free energy, the objective is mainly defined in terms of computational convenience as opposed to a true distance measure between the target and approximating distributions. In this paper, we provide a geometric view of posterior approximation based on a valid distance measure derived from ambient Fisher geometry. Our proposed framework is easily generalizable and can inspire a new class of methods for approximate Bayesian inference.