集体图模型的增量推理
Incremental inference of collective graphical models
摘要：
我们认为增量推理问题的集体动力学的聚合数据。特别是，我们解决的问题，估计聚合边际的马尔可夫链从嘈杂的聚合观测增量（在线）的方式。我们提出了一个滑动窗口Sinkhorn信念传播（SW-SBP）算法，利用滑动窗口过滤器的最近的噪声聚集观测以及编码信息从丢弃的观察。我们的算法是建立在最近提出的多边际最优运输的SBP算法，利用标准的信念传播和Sinkhorn算法来解决推理问题，从聚合数据。我们证明了我们的算法的应用程序，如推断人口流动的聚集观察的性能。
英文摘要：
We consider incremental inference problems from aggregate data for collective dynamics. In particular, we address the problem of estimating the aggregate marginals of a Markov chain from noisy aggregate observations in an incremental (online) fashion. We propose a sliding window Sinkhorn belief propagation (SW-SBP) algorithm that utilizes a sliding window filter of the most recent noisy aggregate observations along with encoded information from discarded observations. Our algorithm is built upon the recently proposed multi-marginal optimal transport based SBP algorithm that leverages standard belief propagation and Sinkhorn algorithm to solve inference problems from aggregate data. We demonstrate the performance of our algorithm on applications such as inferring population flow from aggregate observations.
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。
