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Activity Number: 52
Type: Invited
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: ENAR
Abstract #318013 View Presentation
Title: Bayesian Modeling and Inference for High-Dimensional Spatiotemporal Data Sets
Author(s): Sudipto Banerjee*
Companies: University of California at Los Angeles
Keywords: Bayesian modeling ; Cholesky factorization ; Gaussian Process ; Markov chain Monte Carlo ; Nearest-Neighbor GP ; Spatiotemporal process

With rapid developments in GIS, statisticians today routinely encounter spatial and temporal data containing observations from a large number of spatial locations and time points. However, fitting hierarchical spatial-temporal models often involves expensive matrix computations with complexity increasing in cubic order for the number of spatial locations and temporal points. This renders such models unfeasible for large data sets. I will present two approaches for constructing well-defined spatial-temporal stochastic processes that accrue substantial computational savings. Both these processes can be used as "priors" for spatial-temporal random fields. The first approach constructs a low-rank process operating on a lower-dimensional subspace. The second approach constructs a Nearest-Neighbor Gaussian Process (NNGP) that can be exploited as a dimension-reducing prior embedded within a rich and flexible hierarchical framework to deliver exact Bayesian inference. We compare these methods and demonstrate its use in inferring on the spatial-temporal distribution of ambient air pollution in continental Europe using spatial-temporal regression models with chemistry transport models.

Authors who are presenting talks have a * after their name.

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