论文标题
一个迭代的块粒子滤波器，用于推断具有共享和单位特异性参数的耦合动态系统
An iterated block particle filter for inference on coupled dynamic systems with shared and unit-specific parameters
论文作者
论文摘要
我们考虑推断部分观察到的，随机，相互作用的非线性动态过程的集合。每个过程都用称为单元的标签识别，我们的主要动机是在生物种群系统中产生的，其中单元对应于空间上不同的子群。跨型系统的特征是单个单元内的时间强烈依赖性，并且单位之间的相互作用相对较弱，而这些属性使块粒子过滤器成为基于模拟的可能性评估的有效工具。迭代过滤算法可以促进基于仿真的过滤器的可能性最大化。当参数特定于单位或单位之间共享时，我们引入了适用的迭代块粒子滤波器。我们通过对描述二十个城镇的时空麻疹病例报告数据的耦合流行病学模型进行推断来证明这种算法。
We consider inference for a collection of partially observed, stochastic, interacting, nonlinear dynamic processes. Each process is identified with a label called its unit, and our primary motivation arises in biological metapopulation systems where a unit corresponds to a spatially distinct sub-population. Metapopulation systems are characterized by strong dependence through time within a single unit and relatively weak interactions between units, and these properties make block particle filters an effective tool for simulation-based likelihood evaluation. Iterated filtering algorithms can facilitate likelihood maximization for simulation-based filters. We introduce an iterated block particle filter applicable when parameters are unit-specific or shared between units. We demonstrate this algorithm by performing inference on a coupled epidemiological model describing spatiotemporal measles case report data for twenty towns.
