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Abstract:
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Spatio-temporal data are ubiquitous in the environmental sciences, and their study is important for understanding and predicting a wide variety of processes. One of the chief difficulties in modeling spatial processes that change with time is the complexity of the dependence structures that must describe how such a process varies, and the presence of high-dimensional datasets and prediction domains. It is particularly challenging to specify parameterizations for nonlinear dynamical spatio-temporal models that are simultaneously useful scientifically and efficient computationally. Here, we describe a nonlinear dynamical spatio-temporal model that is motivated by recurrent neural network models, but within a hierarchical Bayesian estimation framework to better quantify uncertainty. The methodology is illustrated with environmental applications.
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