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 language | English |
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Title of host publication | Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP2017 |
Publisher | IEEE |
Number of pages | 7 |
ISBN (Electronic) | 978-1-5090-6341-3 |
DOIs | |
Publication status | Published - 7 Dec 2017 |
MoE publication type | A4 Conference publication |
Event | IEEE International Workshop on Machine Learning for Signal Processing - Tokyo, Japan Duration: 25 Sept 2017 → 28 Sept 2017 Conference number: 27 http://mlsp2017.conwiz.dk/home.htm |
Publication series
Name | IEEE International Workshop on Machine Learning for Signal Processing |
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Publisher | IEEE |
ISSN (Print) | 2161-0363 |
ISSN (Electronic) | 2161-0371 |
Workshop
Workshop | IEEE International Workshop on Machine Learning for Signal Processing |
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Abbreviated title | MLSP |
Country/Territory | Japan |
City | Tokyo |
Period | 25/09/2017 → 28/09/2017 |
Internet address |