On the use of gradient information in Gaussian process quadratures

Jakub Pruher, Simo Särkkä

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)

Abstract

Gaussian process quadrature is a promising alternative Bayesian approach to numerical integration, which offers attractive advantages over its well-known classical counterparts. We show how Gaussian process quadrature can naturally incorporate gradient information about the integrand. These results are applied for the design of transformation of means and covariances of Gaussian random variables. We theoretically analyze connections between our proposed moment transform and the linearization transform based on Taylor series. Numerical experiments on common sensor network nonlinearities show that adding gradient information improves the resulting estimates.

Original languageEnglish
Title of host publicationProceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016
EditorsFrancesco A.N. Palmieri, Aurelio Uncini, Kostas Diamantaras, Jan Larsen
Volume2016-November
ISBN (Electronic)9781509007462
DOIs
Publication statusPublished - 8 Nov 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Salerno, Italy
Duration: 13 Sep 201616 Sep 2016
Conference number: 26
http://mlsp2016.conwiz.dk/home.htm

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing
PublisherIEEE COMPUTER SOCIETY PRESS
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
CountryItaly
CitySalerno
Period13/09/201616/09/2016
Internet address

Keywords

  • Bayesian quadrature
  • derivative
  • Gaussian process quadrature
  • gradient
  • moment transformation

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