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Activity Number: 288 - Advances in Nonlinear and Non-Gaussian Spatio-Temporal Dynamical Models for Environmental and Ecological Processes
Type: Topic Contributed
Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #323046
Title: Nonlinear Dynamical Spatio-Temporal Models and Their Efficient Estimation
Author(s): Christopher Wikle*
Companies: University of Missouri
Keywords: spatio-temporal ; nonlinear ; dynamics ; environmental ; recurrent neural network ; Bayesian
Abstract:

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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