Počet záznamů: 1  

Mixture ratio modeling of dynamic systems

  1. 1.
    SYSNO ASEP0539397
    Druh ASEPJ - Článek v odborném periodiku
    Zařazení RIVJ - Článek v odborném periodiku
    Poddruh JČlánek ve WOS
    NázevMixture ratio modeling of dynamic systems
    Tvůrce(i) Kárný, Miroslav (UTIA-B) RID, ORCID
    Ruman, Marko (UTIA-B)
    Celkový počet autorů2
    Zdroj.dok.International Journal of Adaptive Control and Signal Processing. - : Wiley - ISSN 0890-6327
    Roč. 35, č. 5 (2021), s. 660-675
    Poč.str.16 s.
    Forma vydáníOnline - E
    Jazyk dok.eng - angličtina
    Země vyd.GB - Velká Británie
    Klíč. slovaapproximate Bayesian estimation ; black-box dynamic model ; data stream processing ; universal approximation ; mixture model ; Kullback-Leibler divergence
    Vědní obor RIVBB - Aplikovaná statistika, operační výzkum
    Obor OECDAutomation and control systems
    CEPLTC18075 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy
    Způsob publikováníOmezený přístup
    Institucionální podporaUTIA-B - RVO:67985556
    UT WOS000616106100001
    EID SCOPUS85100778905
    DOI10.1002/acs.3219
    AnotaceAny 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.
    PracovištěÚstav teorie informace a automatizace
    KontaktMarkéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
    Rok sběru2022
    Elektronická adresahttps://onlinelibrary.wiley.com/doi/full/10.1002/acs.3219
Počet záznamů: 1  

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