Work

Evaluating the squared-exponential covariance function in Gaussian processes with integral observations

Year: 2018

Type: preprint

Abstract: This paper deals with the evaluation of double line integrals of the squared exponential covariance function. We propose a new approach in which the double integral is reduced to a single integral using the error function. This single integral is then computed with efficiently implemented numerical techniques. The performance is compared against existing state of the art methods and the results show superior properties in numerical robustness and accuracy per computation time.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1812.07319

Authors: Hendriks, J. N., Jidling, C., Wills, A., Schön, T. B.

Institutions: University of Newcastle Australia, Uppsala University

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Stiftelsen för Strategisk Forskning, Vetenskapsrådet

Awards: G2295429243, NewLEADS - New Directions in Learning Dynamical Systems, G6535064320, G6622944701, Learning flexible models of nonlinear dynamics, Automatisering av systemspecifik modellbaserad inlärning

OpenAlex ID: W2905476986

Cites: 13

Cited by: 8

Related to: 10