提出递归粒子滤波法，高效实现非独立实验的贝叶斯设计
Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design
- 基于非马尔可夫状态空间建模，将策略优化转为最大似然估计
- 计算复杂度最高为 $\mathcal{O}(T^2)$，实验次数越多越高效
- 支持递归更新与轨迹退化抑制，适合连续实验场景
本文提出一种全新的全递归算法 Inside-Out Nested Particle Filter (IO-NPF)，用于非独立设置下的近似序列贝叶斯实验设计。我们将策略优化建模为非马尔可夫状态空间模型中的最大似然估计，实现了最多 $\mathcal{O}(T^2)$ 的计算复杂度（$T$ 为实验次数）。论文提供了理论收敛性保证，并引入反向采样算法以缓解轨迹退化问题。IO-NPF 提供了一种实用、可扩展且可证明一致的序列贝叶斯实验设计方法，在效率上优于现有技术。
原文摘要 · Abstract (English)
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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