SSOR Department Seminar: Fall 2018
Gaussian-Process Approximations for Big Data
Dr. Matthias Katzfuss
Texas A&M University
Thursday, November 15, 2018
10:00 am-11:00 am
Grace Harris Hall
Gaussian processes (GPs) are popular, flexible, and interpretable probabilistic models for functions. GPs are well suited for big data in areas such as machine learning, regression, and geospatial analysis. However, direct application of GPs is computationally infeasible for large datasets. We consider a framework for fast GP inference based on the so-called Vecchia approximation. Our framework contains many popular existing GP approximations as special cases. Representing the models by directed acyclic graphs, we determine the sparsity of the matrices necessary for inference, which leads to new insights regarding the computational properties. Based on these results, we propose novel Vecchia approaches for noisy, non-Gaussian, and massive data. We provide theoretical results, conduct numerical comparisons, and apply the methods to satellite data.
Matthias Katzfuss is an associate professor in the Department of Statistics at Texas A&M University. His research interests include spatial and spatiotemporal statistics, computational statistics, and applications to satellite remote-sensing data. He has received an NSF Career Award and an ASA-ENVR Early Investigator Award.
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