An iterated block particle filter for inference on coupled dynamic systems with shared and unit-specific parameters
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.
Cite
@article{arxiv.2206.03837,
title = {An iterated block particle filter for inference on coupled dynamic systems with shared and unit-specific parameters},
author = {Edward L. Ionides and Ning Ning and Jesse Wheeler},
journal= {arXiv preprint arXiv:2206.03837},
year = {2022}
}