Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design

• URL: http://arxiv.org/abs/2409.05354v1
• Date: Mon, 9 Sep 2024 06:27:54 GMT
• Title: Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design
• Authors: Sahel Iqbal, Hany Abdulsamad, Sara Pérez-Vieites, Simo Särkkä, Adrien Corenflos,
• Abstract summary: Inside-Out Nested Particle Filter (IO-NPF) An algorithm for amortized sequential Bayesian experimental design in the non-exchangeable setting. A backward sampling algorithm to reduce trajectory degeneracy.
• Score: 10.420092609356217
• License: http://creativecommons.org/licenses/by/4.0/
• 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. IO-NPF offers a practical, extensible, and provably consistent approach to sequential Bayesian experimental design, demonstrating improved efficiency over existing methods.

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