Mixture ratio modeling of dynamic systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00539397" target="_blank" >RIV/67985556:_____/21:00539397 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/acs.3219" target="_blank" >https://onlinelibrary.wiley.com/doi/full/10.1002/acs.3219</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/acs.3219" target="_blank" >10.1002/acs.3219</a>
Alternative languages
Result language
angličtina
Original language name
Mixture ratio modeling of dynamic systems
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/LTC18075" target="_blank" >LTC18075: Distributed rational decision making: cooperation aspects</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Adaptive Control and Signal Processing
ISSN
0890-6327
e-ISSN
1099-1115
Volume of the periodical
35
Issue of the periodical within the volume
5
Country of publishing house
GB - UNITED KINGDOM
Number of pages
16
Pages from-to
660-675
UT code for WoS article
000616106100001
EID of the result in the Scopus database
2-s2.0-85100778905