Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design
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Abstract (arXiv)
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) O(T2) 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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 11 of its 32 references that are in the map. First posted on arXiv 9 Sep 2024.
Builds on
11 of 32 references.
References · 11
- 2023Modern Bayesian Experimental Designcited by 125
- 2021Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designcited by 106
- 2021Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoodscited by 61
- 2020Markovian Score Climbing: Variational Inference with KL(p||q)cited by 46
- 2024Optimal experimental design: Formulations and computationscited by 45
- 2016Sequential Bayesian optimal experimental design via approximate dynamic programmingcited by 28
- 2014Particle Gibbs with Ancestor Samplingcited by 21
- 2024Nesting Particle Filters for Experimental Design in Dynamical Systemscited by 12
- 2022On backward smoothing algorithmscited by 7
- 2023State and parameter learning with PaRIS particle Gibbscited by 4
- 2023Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbingcited by 2
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Citing papers per year: 2025: 3.
3 papers.