3:30pm - 4:30pm | Biostat Seminar: Gaussian-Process Approximations for Big Data

Date: 
Wednesday, November 13, 2019

Matthias Katzfuss, Associate Professor
Department of Statistics, Texas A&M University
Wednesday, November 13, 2019
3:30pm - 4:30pm, CHS 33-105A
Refreshments served at 3:00 PM in room 51-254 CHS

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. while protecting against over-confidence.