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Activity Number: 594 - Spatial Risk Assessment with Environmental Applications
Type: Invited
Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #323766 View Presentation
Title: Bayesian Computing and Modeling for Nearest-Neighbor Gaussian Process Models
Author(s): Sudipto Banerjee*
Companies: UCLA Fielding School of Public Health
Keywords: Bayesian inference ; Hierarchical modeling ; Nearest-Neighbor GP ; Scalable GP ; Spatiotemporal processes
Abstract:

With the growing capabilities of Geographic Information Systems (GIS) and reated 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. This talk will focus upon a variety of modeling and computational strategies to implement massively scalable Gaussian process models for Bayesian inference in settings involving over 6 million locations.


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

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