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.

翻译：我们认为,收集部分观测的、随机的、互动的、非线性动态过程的推论是:每个过程都有一个称为其单位的标签,而我们的主要动机是生物元人口系统,其中一个单位与空间上不同的亚人口相对应;元人口系统的特征是,在一个单位内一段时间内高度依赖性很强,各单元之间的互动相对薄弱,这些特性使块粒子过滤器成为模拟概率评估的有效工具;循环过滤算法可以促进模拟过滤器的可能性最大化;我们引入一个循环的块粒子过滤器,当参数是单位特定或单位之间共享时适用;我们通过对描述20个城镇脑膜麻疹病例数据的混合流行病学模型进行推断,以此来证明这一算法。