标题： 结构化过滤 显示英文标题

标题： Structured Filtering

摘要： 物理学中参数估计现有的序贯蒙特卡洛方法面临的重大挑战源于现有方法无法稳健地处理具有不同机制且以同等概率产生结果的实验。 我们通过提出一种粒子滤波形式来解决这个问题，在重采样之前对构成序贯蒙特卡洛后验近似值的粒子进行聚类。 通过一种新的图形化方式思考此类模型，我们能够设计出一种基于人工智能的策略，自动学习后验支持中聚类的形状和数量。 我们通过将其应用于随机间隙估计和一种低电路深度相位估计的形式来展示我们的方法的强大之处，物理学文献中的现有方法要么表现差很多，要么完全失效。 显示英文摘要

摘要： A major challenge facing existing sequential Monte-Carlo methods for parameter estimation in physics stems from the inability of existing approaches to robustly deal with experiments that have different mechanisms that yield the results with equivalent probability. We address this problem here by proposing a form of particle filtering that clusters the particles that comprise the sequential Monte-Carlo approximation to the posterior before applying a resampler. Through a new graphical approach to thinking about such models, we are able to devise an artificial-intelligence based strategy that automatically learns the shape and number of the clusters in the support of the posterior. We demonstrate the power of our approach by applying it to randomized gap estimation and a form of low circuit-depth phase estimation where existing methods from the physics literature either exhibit much worse performance or even fail completely.