Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes.

ABSTRACT: In geostatistical problems with massive sample size, Gaussian processes can be approximated using sparse directed acyclic graphs to achieve scalable O(n) computational complexity. In these models, data at each location are typically assumed conditionally dependent on a small set of parents which usually include a subset of the nearest neighbors. These methodologies often exhibit excellent empirical performance, but the lack of theoretical validation leads to unclear guidance in specifying the underlying graphical model and sensitivity to graph choice. We address these issues by introducing radial neighbors Gaussian processes (RadGP), a class of Gaussian processes based on directed acyclic graphs in which directed edges connect every location to all of its neighbors within a predetermined r

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC11993192 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature