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Minimum Expected Relative Entropy Principle
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SYSNO ASEP 0525233 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Minimum Expected Relative Entropy Principle Author(s) Kárný, Miroslav (UTIA-B) RID, ORCID Source Title Proceedings of the 18th European Control Conference (ECC). - Saint Petersburg : European Union Control Association (EUCA), 2020 - ISBN 978-390714401-5 Pages s. 35-40 Number of pages 6 s. Publication form Medium - C Action The European Control Conference (ECC 2020) Event date 12.05.2020 - 15.05.2020 VEvent location Saint Petersburg Country RU - Russian Federation Event type WRD Language eng - English Country RU - Russian Federation Keywords minimum relative entropy principle ; uncertain prior probability ; forgetting ; fully probabilistic design ; abrupt parameter changes Subject RIV BC - Control Systems Theory OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects LTC18075 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Institutional support UTIA-B - RVO:67985556 UT WOS 000613138000007 EID SCOPUS 85090153735 DOI 10.23919/ECC51009.2020.9143856 Annotation Stochastic filtering estimates a timevarying (multivariate) parameter (a hidden variable) from noisy observations. It needs both observation and parameter evolution models. The latter is often missing or makes the estimation too complex. Then, the axiomatic minimum relative entropy (MRE) principle completes the posterior probability density (pd) of the parameter. The MRE principle recommends to modify a prior guess of the constructed pd to the smallest extent enforced by new observations. The MRE principle does not deal with a generic uncertain prior guess. Such uncertainty arises, for instance, when the MRE principle is used recursively. The paper fills this gap. The proposed minimum expected relative entropy (MeRE) principle: (a) makes Bayesian estimation less sensitive to the choice of the prior pd. (b) provides a stabilised parameter tracking with a data-dependent forgetting that copes with abrupt parameter changes. (c) applies in all cases exploiting MRE, for instance, in stochastic modelling. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2021
Number of the records: 1