JADAI：联合摊销自适应设计与贝叶斯推理
JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
AI总结：
针对设计变量可优化以最大化信息增益的参数估计问题，引入JADAI框架，通过端到端训练策略、历史网络和推理网络联合摊销贝叶斯自适应设计与推理，在标准基准测试中性能优越。
摘要：
我们考虑设计变量可主动优化以最大化信息增益的参数估计问题。为此，我们引入JADAI，一个通过端到端训练策略、历史网络和推理网络来联合摊销贝叶斯自适应设计和推理的框架。这些网络最小化一个通用损失，该损失汇总了沿实验序列后验误差的增量减少。推理网络用基于扩散的后验估计器实例化，该估计器可在每个实验步骤近似高维多峰后验。在标准自适应设计基准中，JADAI实现了优越或有竞争力的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
