用模拟推断提升贝叶斯实验设计效率，性能领先超22%。
Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Design
- 利用神经似然估计构建新型信息增益估计算法
- 在标准基准上性能较现有方法最高提升22%
- 多起点并行梯度上升显著改善优化可靠性
贝叶斯最优实验设计（BOED）旨在最大化实验的期望信息增益（EIG），但许多场景下似然函数不可解析。模拟推断（SBI）为此提供了强大工具。然而，现有工作仅关联单一对比型EIG界。本文揭示EIG存在多种可直接利用现代SBI密度估计器（如神经后验、似然、比值估计）的表达形式。基于此，我们提出一种基于神经似然估计的新EIG估计算法。进一步发现，梯度优化是当前方法的关键瓶颈，提出简单多起点并行梯度上升策略，显著提升可靠性和性能。在标准BOED基准上，本方法性能匹配或超越现有最先进方法，最高提升达22%。
原文摘要 · Abstract (English)
Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is intractable. Simulation-based inference (SBI) provides powerful tools for this regime. However, existing work explicitly connecting SBI and BOED is restricted to a single contrastive EIG bound. We show that the EIG admits multiple formulations which can directly leverage modern SBI density estimators, encompassing neural posterior, likelihood, and ratio estimation. Building on this perspective, we define a novel EIG estimator using neural likelihood estimation. Further, we identify optimization as a key bottleneck of gradient based EIG maximization and show that a simple multi-start parallel gradient ascent procedure can substantially improve reliability and performance. With these innovations, our SBI-based BOED methods are able to match or outperform by up to $22\%$ existing state-of-the-art approaches across standard BOED benchmarks.
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