Statistics Colloquium
March 24 (Friday) 11 a.m.
Alavi Commons Room, 6625 Everett Tower

High-Dimensional Bayesian Geostatistics

Sudipto Banerjee, PhD
Professor and Chair
Dept. of Biostatistics
UCLA Fielding School of Public Health

With the growing capabilities of Geographic Information Systems (GIS) and related software, statisticians today routinely encounter spatial data containing observations from a massive number of locations and time points. Important areas of application include environmental exposure assessment and construction of risk maps based upon massive amounts of spatiotemporal data. Spatiotemporal process models have been, and continue to be, widely deployed by researchers to better understand the complex nature of spatial and temporal variability. However, fitting hierarchical spatiotemporal models is computationally onerous with complexity increasing in cubic order for the number of spatial locations and temporal points. Massively scalable Gaussian process models, such as the Nearest-Neighbor Gaussian Process (NNGP), that can be estimated using algorithms requiring floating point operations (flops) and storage linear in the number of spatiotemporal points. The focus will be on a variety of modeling and computational strategies to implement massively scalable Gaussian process models and conduct Bayesian inference in settings involving massive amounts of spatial data.

Sudipto Banerjee graduated with a Ph.D. in Statistics from the University of Connecticut in 2000. He is currently professor and Chair of Biostatistics at the UCLA Fielding School of Public Health. His research interests include statistical modeling and analysis of geographically referenced datasets, Bayesian statistics (theory and methods) and hierarchical modelling, statistical computing and related software development.
All statistics graduate students are expected to attend.


Past colloquiums


Department of Statistics
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Western Michigan University
Kalamazoo MI 49008-5152 USA
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