High-Dimensional Bayesian Geostatistics

Add to your list(s) Download to your calendar using vCal

• Sudipto Banerjee (UCLA)
• Monday 23 January 2017, 16:00-17:00
• MR12, Centre for Mathematical Sciences, Wilberforce Road, Cambridge..

If you have a question about this talk, please contact Quentin Berthet.

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatial-temporal process models have become widely deployed statistical tools for researchers to better understanding the complex nature of spatial and temporal variability. 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. In this talk, I will present some approaches for constructing well-defined spatial-temporal stochastic processes that accrue substantial computational savings. These processes can be used as “priors” for spatial-temporal random fields. Specifically, we will discuss and distinguish between two paradigms: low-rank and sparsity and argue in favor of the latter for achieving massively scalable inference. We construct a well-defined Nearest-Neighbor Gaussian Process (NNGP) that can be exploited as a dimension-reducing prior embedded within a rich and flexible hierarchical modeling framework to deliver exact Bayesian inference. Both these approaches lead to algorithms with floating point operations (flops) that are linear in the number of spatial locations (per iteration). We compare these methods and demonstrate their use in a number of applications and, in particular, in inferring on the spatial-temporal distribution of air pollution in continental Europe using spatial-temporal regression models in conjunction with chemistry transport models.

This is based upon joint work with Abhirup Datta (Johns Hopkins University) and Andrew O. Finley (Michigan State University)

This talk is part of the Statistics series.

This talk is included in these lists:

• All CMS events
• All Talks (aka the CURE list)
• CMS Events
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• DPMMS Lists
• DPMMS info aggregator
• DPMMS lists
• Guy Emerson's list
• Hanchen DaDaDash
• Interested Talks
• MR12, Centre for Mathematical Sciences, Wilberforce Road, Cambridge.
• Machine Learning
• School of Physical Sciences
• Statistical Laboratory info aggregator
• Statistics
• Statistics Group
• bld31
• custom
• rp587

Note that ex-directory lists are not shown.