Classical quadrature rules via Gaussian processes

Toni Karvonen, Simo Särkkä

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

20 Citations (Scopus)
362 Downloads (Pure)

Abstract

In an extension to some previous work on the topic, we show how all classical polynomial-based quadrature rules can be interpreted as Bayesian quadrature rules if the covariance kernel is selected suitably. As the resulting Bayesian quadrature rules have zero posterior integral variance, the results of this article are mostly of theoretical interest in clarifying the relationship between the two different approaches to numerical integration.
Original languageEnglish
Title of host publicationProceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP2017
PublisherIEEE
Number of pages7
ISBN (Electronic)978-1-5090-6341-3
DOIs
Publication statusPublished - 7 Dec 2017
MoE publication typeA4 Conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Tokyo, Japan
Duration: 25 Sept 201728 Sept 2017
Conference number: 27
http://mlsp2017.conwiz.dk/home.htm

Publication series

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

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
Country/TerritoryJapan
CityTokyo
Period25/09/201728/09/2017
Internet address

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