IMAGe Brown Bag Seminar - Recent Advances in Quantifying Uncertainty in Nonlinear Dynamical Spatio-Temporal Statistical Models

03/02/2017 -
12:00pm to 1:00pm
Mesa Lab - Chapman Room


Christopher K. Wikle

Curators’ Distinguished Professor

Department of Statistics

University of Missouri


Spatio-temporal data are ubiquitous in the environmental sciences, and their study is important for understanding and predicting a wide variety of processes of interest to meteorologists and climate scientists.  One of the primary 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. Much of the methodological development in recent years has considered either efficient moment-based approaches or spatio-temporal dynamical models. To date, most of the focus on statistical methods for dynamic spatio-temporal processes has been on linear models. Even in these relatively simple models, there are significant challenges in specifying parameterizations that are simultaneously useful scientifically and efficient computationally.  This talk presents some recent attempts to place these models, some of which were motivated in the atmospheric sciences, into a more rigorous uncertainty quantification framework.  Examples of such methods include quadratically nonlinear, analog, and agent-based models.


Thursday, March 2, 2017


Mesa Lab, Chapman Room

(Bring your lunch)