Title: Inference on coupled stochastic dynamic models using a guided intermediate resampling filter
Abstract: In this talk, I will demonstrate a likelihood-based inference method for spatio-temporal stochastic dynamic systems using a mechanistic model. The model, the nature of the dynamics, and the volume of the data may pose a unique set of computational challenges. Using the example of the population dynamics of infectious diseases in a network of linked regions, I will first introduce the class of models we consider. These models describe coupled, partially observed Markov processes that are highly non-linear and non-Gaussian. The models are also defined implicitly by computer simulation algorithms that reflect the modeler's mechanistic understanding of the system. I will explain why widely used methods, such as the standard particle filter methods, the ensemble Kalman filter, and the implicit particle filter, may not work for these models. Next, I will present a new method, called a guided intermediate resampling filter (GIRF), that addresses the challenges. This method can be readily combined with parameter estimation methodologies to enable likelihood-based inference for highly nonlinear, coupled spatiotemporal systems.