Approximate Bayesian recursive estimation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F14%3A00425539" target="_blank" >RIV/67985556:_____/14:00425539 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.ins.2014.01.048" target="_blank" >http://dx.doi.org/10.1016/j.ins.2014.01.048</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2014.01.048" target="_blank" >10.1016/j.ins.2014.01.048</a>
Alternative languages
Result language
angličtina
Original language name
Approximate Bayesian recursive estimation
Original language description
Bayesian learning provides a firm theoretical basis of the design and exploitation of algorithms in data-streams processing (preprocessing, change detection, hypothesis testing, clustering, etc.). Primarily, it relies on a recursive parameter estimationof a firmly bounded complexity. As a rule, it has to approximate the exact posterior probability density (pd), which comprises unreduced information about the estimated parameter. In the recursive treatment of the data stream, the latest approximate pd is usually updated using the treated parametric model and the newest data and then approximated. The fact that approximation errors may accumulate over time course is mostly neglected in the estimator design and, at most, checked ex post. The paper inspects the estimator design with respect to the error accumulation and concludes that a sort of forgetting (pd flattening) is an indispensable part of a reliable approximate recursive estimation.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA13-13502S" target="_blank" >GA13-13502S: Fully Probabilistic Design of Dynamic Decision Strategies for Imperfect Participants in Market Scenarios</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information Sciences
ISSN
0020-0255
e-ISSN
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Volume of the periodical
285
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
100-111
UT code for WoS article
000342540700007
EID of the result in the Scopus database
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