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Abstract
Partial differential equations are a mathematical tool widely used to model phenomena in several different fields. A stochastic partial differential equation (SPDE) introduces random forcing to take the nature of real-world observations.
We will introduce an SPDE approach to work with models for temporal, spatial, and spacetime domains. Such an approach has a direct link with the statistical models based on Gaussian random field (GRF) models. The main goal of this talk is to introduce this direct link, its computational benefits, and the recent developments based on this framework. We also will show illustrative applications in statistical models for real-world datasets.
Brief Biography
Elias completed his PhD at the Norwegian University of Science and Technology in Trondheim, Norway. Former assistant/adjunct professor at the Federal University of Paraná, in Curitiba Brazil. Former postdoctoral fellow in Halifax, Canada.
He currently works as a postdoctoral fellow in Professor Håvard Rue's bayescomp research group at the CEMSE/KAUST.