联合优化实验设计与推断，提升参数估计效率。
JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
- 端到端训练策略、历史网络与推断网络协同工作
- 在多个基准测试中表现优于或媲美现有方法
- 基于扩散模型的后验估计支持高维多模态分布
我们研究参数估计问题，其中设计变量可主动优化以最大化信息增益。为此，提出JADAI框架，通过联合摊销贝叶斯自适应设计与推断，训练策略网络、历史网络和推断网络端到端完成。各网络最小化一个通用损失函数，该函数聚合实验序列中后验误差的增量降低。推断网络采用基于扩散模型的后验估计器，可在每个实验步骤近似高维及多模态后验分布。在标准自适应设计基准上，JADAI表现出优越或具有竞争力的性能。
原文摘要 · Abstract (English)
We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain. To this end, we introduce JADAI, a framework that jointly amortizes Bayesian adaptive design and inference by training a policy, a history network, and an inference network end-to-end. The networks minimize a generic loss that aggregates incremental reductions in posterior error along experimental sequences. Inference networks are instantiated with diffusion-based posterior estimators that can approximate high-dimensional and multimodal posteriors at every experimental step. Across standard adaptive design benchmarks, JADAI achieves superior or competitive performance.
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