Classical quadrature rules via Gaussian processes

Toni Karvonen; Simo Särkkä

doi:10.1109/MLSP.2017.8168195

Classical quadrature rules via Gaussian processes

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

20 Sitaatiot (Scopus)

357 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP2017
Kustantaja	IEEE
Sivumäärä	7
ISBN (elektroninen)	978-1-5090-6341-3
DOI - pysyväislinkit	https://doi.org/10.1109/MLSP.2017.8168195
Tila	Julkaistu - 7 jouluk. 2017
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Workshop on Machine Learning for Signal Processing - Tokyo, Japani Kesto: 25 syysk. 2017 → 28 syysk. 2017 Konferenssinumero: 27 http://mlsp2017.conwiz.dk/home.htm

Julkaisusarja

Nimi	IEEE International Workshop on Machine Learning for Signal Processing
Kustantaja	IEEE
ISSN (painettu)	2161-0363
ISSN (elektroninen)	2161-0371

Workshop

Workshop	IEEE International Workshop on Machine Learning for Signal Processing
Lyhennettä	MLSP
Maa/Alue	Japani
Kaupunki	Tokyo
Ajanjakso	25/09/2017 → 28/09/2017
www-osoite	http://mlsp2017.conwiz.dk/home.htm

Pääsy asiakirjaan

10.1109/MLSP.2017.8168195

KarvonenSarkka2017-MLSP
Copyright 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 378 KB

Siteeraa tätä

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title = "Classical quadrature rules via Gaussian processes",

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.",

author = "Toni Karvonen and Simo S{\"a}rkk{\"a}",

year = "2017",

month = dec,

day = "7",

doi = "10.1109/MLSP.2017.8168195",

language = "English",

series = "IEEE International Workshop on Machine Learning for Signal Processing",

publisher = "IEEE",

booktitle = "Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP2017",

address = "United States",

note = "IEEE International Workshop on Machine Learning for Signal Processing , MLSP ; Conference date: 25-09-2017 Through 28-09-2017",

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Karvonen, T & Särkkä, S 2017, Classical quadrature rules via Gaussian processes. julkaisussa Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP2017. IEEE International Workshop on Machine Learning for Signal Processing, IEEE, IEEE International Workshop on Machine Learning for Signal Processing , Tokyo, Japani, 25/09/2017. https://doi.org/10.1109/MLSP.2017.8168195

Classical quadrature rules via Gaussian processes. / Karvonen, Toni; Särkkä, Simo.
Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP2017. IEEE, 2017. (IEEE International Workshop on Machine Learning for Signal Processing).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Classical quadrature rules via Gaussian processes

AU - Karvonen, Toni

AU - Särkkä, Simo

N1 - Conference code: 27

PY - 2017/12/7

Y1 - 2017/12/7

N2 - 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.

AB - 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.

U2 - 10.1109/MLSP.2017.8168195

DO - 10.1109/MLSP.2017.8168195

M3 - Conference article in proceedings

T3 - IEEE International Workshop on Machine Learning for Signal Processing

BT - Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP2017

PB - IEEE

T2 - IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 25 September 2017 through 28 September 2017

ER -

Classical quadrature rules via Gaussian processes

Abstrakti

Julkaisusarja

Workshop

Pääsy asiakirjaan

Sormenjälki

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