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Mixture ratio modeling of dynamic systems
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SYSNO ASEP 0539397 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Mixture ratio modeling of dynamic systems Author(s) Kárný, Miroslav (UTIA-B) RID, ORCID
Ruman, Marko (UTIA-B)Number of authors 2 Source Title International Journal of Adaptive Control and Signal Processing. - : Wiley - ISSN 0890-6327
Roč. 35, č. 5 (2021), s. 660-675Number of pages 16 s. Publication form Online - E Language eng - English Country GB - United Kingdom Keywords approximate Bayesian estimation ; black-box dynamic model ; data stream processing ; universal approximation ; mixture model ; Kullback-Leibler divergence Subject RIV BB - Applied Statistics, Operational Research OECD category Automation and control systems R&D Projects LTC18075 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000616106100001 EID SCOPUS 85100778905 DOI 10.1002/acs.3219 Annotation Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022 Electronic address https://onlinelibrary.wiley.com/doi/full/10.1002/acs.3219
Number of the records: 1