论文标题
通过连续观测的聚合隐藏的马尔可夫模型过滤
Filtering for Aggregate Hidden Markov Models with Continuous Observations
论文作者
论文摘要
我们考虑了大人群的一类过滤问题，其中每个人都由同一隐藏的马尔可夫模型（HMM）建模。在本文中，我们专注于具有离散状态空间和连续观察空间的HMM中的聚集推理问题。连续观察的汇总方式使得个人与测量结果无法区分。我们提出了一种称为连续观察集体前向算法的聚集推理算法。它扩展了最近提出的集体前回发算法，用于对HMM的汇总推断，并与连续观察的情况进行离散观察。通过几个数值实验来说明该算法的功效。
We consider a class of filtering problems for large populations where each individual is modeled by the same hidden Markov model (HMM). In this paper, we focus on aggregate inference problems in HMMs with discrete state space and continuous observation space. The continuous observations are aggregated in a way such that the individuals are indistinguishable from measurements. We propose an aggregate inference algorithm called continuous observation collective forward-backward algorithm. It extends the recently proposed collective forward-backward algorithm for aggregate inference in HMMs with discrete observations to the case of continuous observations. The efficacy of this algorithm is illustrated through several numerical experiments.
