将模拟推断与贝叶斯实验设计结合，提升复杂模型的推断效率。
Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design
- 通过互信息边界连接模拟推断与变分推断方法
- 实现实验设计与推断函数的联合优化，提升推断精度
- 在流行病学和生物学模拟中验证效果显著
模拟推断（SBI）是一种用于复杂科学模型中难以求解逆问题的推断方法。贝叶斯最优实验设计（BOED）旨在高效利用实验资源以获得更优推断结果。已有多种基于随机梯度的BOED方法被提出，作为贝叶斯优化及其他实验设计启发式方法的替代方案，以最大化实验中的信息增益。本文揭示了SBI与基于随机梯度的变分推断方法之间通过互信息界建立的联系，使得BOED可直接应用于SBI场景，形成SBI-BOED框架。该框架支持同时优化实验设计与近似推断函数。我们在标准SBI任务中分析了朴素设计优化的缺陷，并展示了合理设计分布对BOED的重要作用。在流行病学与生物学的真实模拟器上对比测试表明，该方法显著提升了推断性能。
原文摘要 · Abstract (English)
Simulation-based inference (SBI) is a method to perform inference on a variety of complex scientific models with challenging inference (inverse) problems. Bayesian Optimal Experimental Design (BOED) aims to efficiently use experimental resources to make better inferences. Various stochastic gradient-based BOED methods have been proposed as an alternative to Bayesian optimization and other experimental design heuristics to maximize information gain from an experiment. We demonstrate a link via mutual information bounds between SBI and stochastic gradient-based variational inference methods that permits BOED to be used in SBI applications as SBI-BOED. This link allows simultaneous optimization of experimental designs and optimization of amortized inference functions. We evaluate the pitfalls of naive design optimization using this method in a standard SBI task and demonstrate the utility of a well-chosen design distribution in BOED. We compare this approach on SBI-based models in real-world simulators in epidemiology and biology, showing notable improvements in inference.
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