quadratic nonlinearity

Monday, April 23, 2018 - 9:10am - 10:00am
Chris Wikle (University of Missouri)
Spatio-temporal data are ubiquitous in engineering and the 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 complex datasets and large prediction domains.
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