Abstract
The aim of this article is to design a moment transformation for Student-t distributed random variables, which is able to account for the error in the numerically computed mean. We employ Student-t process quadrature, an instance of Bayesian quadrature, which allows us to treat the integral itself as a random variable whose variance provides information about the incurred integration error. Advantage of the Student-t process quadrature over the traditional Gaussian process quadrature, is that the integral variance depends also on the function values, allowing for a more robust modelling of the integration error. The moment transform is applied in nonlinear sigma-point filtering and evaluated on two numerical examples, where it is shown to outperform the state-of-the-art moment transforms.
Original language | English |
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Title of host publication | 20th International Conference on Information Fusion, Fusion 2017 - Proceedings |
Publisher | IEEE |
Pages | 875-882 |
Number of pages | 8 |
ISBN (Electronic) | 978-0-9964-5270-0 |
DOIs | |
Publication status | Published - Jul 2017 |
MoE publication type | A4 Article in a conference publication |
Event | International Conference on Information Fusion - Xian, China, Xian, China Duration: 10 Jul 2017 → 13 Jul 2017 Conference number: 20 http://www.fusion2017.org/ |
Conference
Conference | International Conference on Information Fusion |
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Abbreviated title | FUSION |
Country/Territory | China |
City | Xian |
Period | 10/07/2017 → 13/07/2017 |
Internet address |
Fingerprint
Dive into the research topics of 'Student-t process quadratures for filtering of non-linear systems with heavy-tailed noise'. Together they form a unique fingerprint.Prizes
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Tammy L. Blair Best Student Paper Award, First Runner-Up
Prüher, J. (Recipient), Tronarp, F. (Recipient), Karvonen, T. (Recipient), Särkkä, Simo (Recipient) & Straka, O. (Recipient), 13 Jul 2017
Prize: Award or honor granted for a specific work
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