Variational Bayesian Optimal Experimental Design with Normalizing Flows

arXiv:2404.13056v1 Announce Type: new
Abstract: Bayesian optimal experimental design (OED) seeks experiments that maximize the expected information gain (EIG) in model parameters. Directly estimating the EIG using nested Monte Carlo is computationally expensive and requires an explicit likelihood. Variational OED (vOED), in contrast, estimates a lower bound of the EIG without likelihood evaluations by approximating the posterior distributions with variational forms, and then tightens the bound by optimizing its variational parameters. We introduce the use of normalizing flows (NFs) for …

abstract arxiv bayesian contrast cs.ce cs.lg design experimental information likelihood optimal experimental design parameters stat.co stat.me stat.ml type