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An Iterated Block Particle Filter for Inference on Coupled Dynamic Systems With Shared and Unit-Specific Parameters
- Source :
- Statistica Sinica.
- Publication Year :
- 2024
- Publisher :
- Statistica Sinica (Institute of Statistical Science), 2024.
-
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.
Details
- ISSN :
- 10170405
- Database :
- OpenAIRE
- Journal :
- Statistica Sinica
- Accession number :
- edsair.doi.dedup.....669bbb401e931a23dabfbc89f4f0c75b