Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design

Sahel Iqbal, Hany Abdulsamad, Sara Perez-Vieites, Simo Särkkä, Adrien Corenflos

Keywords: Bayesian experimental design, sequential Monte Carlo, policy optimization.

TL;DR: A fully recursive, nested particle filtering algorithm for amoritized sequential Bayesian experimental design.

Abstract: This paper introduces the Inside-Out Nested Particle Filter (IO-NPF), a novel fully recursive algorithm for amortized sequential Bayesian experimental design in the non-exchangeable setting. We frame policy optimization as maximum likelihood estimation in a non-Markovian state-space model, achieving (at most) $\mathcal{O}(T^2)$ computational complexity in the number of experiments. We provide theoretical convergence guarantees and introduce a backward sampling algorithm to reduce trajectory degeneracy. The IO-NPF offers a practical, extensible, and provably consistent approach to sequential Bayesian experimental design, demonstrating improved efficiency over existing methods.

Submission Number: 115