Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design
<|reference_start|>Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design: 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.<|reference_end|>
@article{iqbal2024recursive,
    title={Recursive Nested Filtering for Efficient Amortized Bayesian Experimental
  Design},
    author={Sahel Iqbal, Hany Abdulsamad, Sara P'erez-Vieites, Simo S"arkk"a,
  Adrien Corenflos},
    journal={arXiv preprint arXiv:2409.05354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05354},
    primaryClass={stat.ML cs.LG stat.ME}
}