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arXiv · 2206.03837

An iterated block particle filter for inference on coupled dynamic systems with shared and unit-specific parameters

Edward L. Ionides·Ning Ning·Jesse Wheeler
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Abstract

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.

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Edward L. Ionides, Ning Ning, Jesse Wheeler. 2022-06-08. An iterated block particle filter for inference on coupled dynamic systems with shared and unit-specific parameters. https://arxiv.org/abs/2206.03837

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