Daniel Sanz-Alonso, University of Chicago, Department of Statistics and CCAM, Finite Element and Graphical Representations of Gaussian Processes
SAS 4201Gaussian processes (GPs) are popular models for random functions in computational and applied mathematics, statistics, machine learning and data science. However, GP methodology scales poorly to large data-sets due to the need to factorize a dense covariance matrix. In spatial statistics, a standard approach to surmount this challenge is to represent Matérn GPs using finite…