论文标题
集体图形模型的增量推断
Incremental inference of collective graphical models
论文作者
论文摘要
我们考虑从聚合数据中的集体动力学问题中的推理问题。特别是，我们解决了以渐进（在线）方式估算马尔可夫链的总边缘的问题。我们提出了一种滑动窗口sindhorn信念传播（SW-SBP）算法，该算法利用了最新的噪声聚合观测值的滑动窗口滤波器以及从丢弃的观测值中的编码信息。我们的算法建立在最近提出的基于基于最佳运输的多核心最佳运输算法的基础上，该算法利用标准信念传播和sndhorn算法从聚合数据中解决推理问题。我们证明了我们的算法在应用程序上的应用中的性能，例如从总观测中推断人口流量。
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.
