动态系统树
Dynamical Systems Trees
摘要：
我们提出了动态系统树（DST）作为一个灵活的一类模型，用于描述多个过程，通过层次结构的聚合父链相互作用。DST将卡尔曼滤波器、隐马尔可夫模型和非线性动力系统扩展到交互式群体场景。各种单独的进程作为社区和子社区在时间上展开的树结构中进行交互。为了适应非线性的时间活动，每个单独的叶过程被建模为一个动态系统，包含离散和/或连续的隐藏状态与离散和/或高斯发射。随后的更高级别的父进程就像隐马尔可夫模型一样，并调解叶进程之间或层次结构中其他父进程之间的交互。聚合器链是子进程的父进程，它们组合和调解子进程，产生紧凑的整体参数化。我们提供了一个有效的结构化平均场算法的任意DST拓扑结构的易处理的推理和学习算法。通过对基因表达数据的实验和对美式足球比赛中的群体行为进行建模，证明了DST的多种适用性。
英文摘要：
We propose dynamical systems trees (DSTs) as a flexible class of models for describing multiple processes that interact via a hierarchy of aggregating parent chains. DSTs extend Kalman filters, hidden Markov models and nonlinear dynamical systems to an interactive group scenario. Various individual processes interact as communities and sub-communities in a tree structure that is unrolled in time. To accommodate nonlinear temporal activity, each individual leaf process is modeled as a dynamical system containing discrete and/or continuous hidden states with discrete and/or Gaussian emissions. Subsequent higher level parent processes act like hidden Markov models and mediate the interaction between leaf processes or between other parent processes in the hierarchy. Aggregator chains are parents of child processes that they combine and mediate, yielding a compact overall parameterization. We provide tractable inference and learning algorithms for arbitrary DST topologies via an efficient structured mean-field algorithm. The diverse applicability of DSTs is demonstrated by experiments on gene expression data and by modeling group behavior in the setting of an American football game.
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。
