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How consistent is my model with the data?: Information-theoretic model check
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The choice of model class is fundamental in statistical learning and system identification, no matter whether the class is derived from physical principles or is a generic black-box. We develop a method to evaluate the specified model class by assessing its capability of reproducing data that is similar to the observed data record. This model check is based on the information-theoretic properties of models viewed as data generators and is applicable to e.g. sequential data and nonlinear dynamical models. The method can be understood as a specific two-sided posterior predictive test. We apply the information-theoretic model check to both synthetic and real data and compare it with a classical whiteness test.

Place, publisher, year, edition, pages
2018. p. 407-412
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
National Category
Probability Theory and Statistics Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-368622DOI: 10.1016/j.ifacol.2018.09.179ISI: 000446599200070OAI: oai:DiVA.org:uu-368622DiVA, id: diva2:1268453
Conference
SYSID 2018, July 9–11, Stockholm, Sweden
Funder
Swedish Research Council, 2016-06079Swedish Research Council, 621-2014-5874Swedish Foundation for Strategic Research , RIT15-0012Available from: 2018-10-08 Created: 2018-12-05 Last updated: 2018-12-14Bibliographically approved

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Svensson, AndreasZachariah, DaveSchön, Thomas B.
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CiteExportLink to record
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  • apa
  • ieee
  • modern-language-association-8th-edition
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  • fi-FI
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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