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Abstract:
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Statistical models using partial di?erential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scienti?c literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Speci?cally, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation.
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