Classical quadrature rules via Gaussian processes

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A4 Artikkeli konferenssijulkaisussa
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Date

2017-12-07

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Department of Electrical Engineering and Automation

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en

Pages

7

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

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.

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DOI

10.1109/MLSP.2017.8168195

Citation

Karvonen, T & Särkkä, S 2017, Classical quadrature rules via Gaussian processes. in 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, Japan, 25/09/2017. https://doi.org/10.1109/MLSP.2017.8168195

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https://urn.fi/URN:NBN:fi:aalto-201803161723

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[article-cris] Sähkötekniikan korkeakoulu / ELEC