Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities

arXiv:2402.15053v1 Announce Type: cross
Abstract: We study the problem of selecting $k$ experiments from a larger candidate pool, where the goal is to maximize mutual information (MI) between the selected subset and the underlying parameters. Finding the exact solution is to this combinatorial optimization problem is computationally costly, not only due to the complexity of the combinatorial search but also the difficulty of evaluating MI in nonlinear/non-Gaussian settings. We propose greedy approaches based on new computationally inexpensive lower bounds for …

abstract arxiv bayesian cs.lg design experimental information optimal experimental design optimization parameters pool solution stat.me stat.ml study type