Bernstein Flows for Flexible Posteriors in Variational Bayes

arXiv:2202.05650v2 Announce Type: replace-cross
Abstract: Variational inference (VI) is a technique to approximate difficult to compute posteriors by optimization. In contrast to MCMC, VI scales to many observations. In the case of complex posteriors, however, state-of-the-art VI approaches often yield unsatisfactory posterior approximations. This paper presents Bernstein flow variational inference (BF-VI), a robust and easy-to-use method, flexible enough to approximate complex multivariate posteriors. BF-VI combines ideas from normalizing flows and Bernstein polynomial-based transformation models. In benchmark experiments, we compare BF-VI …

abstract art arxiv bayes case compute contrast cs.lg flow inference mcmc optimization paper posterior state stat.ml type