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Tr. SPIIRAN, 2012, Issue 23, Pages 254–295 (Mi trspy561)  

This article is cited in 8 scientific papers (total in 8 papers)

Measures of truth and probabilistic graphical models for representation of knowledge with uncertainty

A. A. Fil'chenkovab

a St. Petersburg Institute for Informatics and Automation of RAS
b St. Petersburg State University, Department of Mathematics and Mechanics

Abstract: For representation of knowledge with uncertainty both a mathematical formalism allowing to describe and handle uncertainty, and a theoretical computer model, limiting the requirement for such representation and processing of memory and time, are required. The paper gives an overview of the main truth measures including probability measure that being applied in artificial intelligence to represent uncertainty, and probabilistic graphical models which allow to limit the growth of processing algorithms complicity and memory requirements for representation of knowledge with uncertainty by means of computation localisation.

Keywords: Bayesian networks, internal and external measures, probabilistical graphic models, knowledge with uncertainty, measures of truth.

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Received: 10.12.2012

Citation: A. A. Fil'chenkov, “Measures of truth and probabilistic graphical models for representation of knowledge with uncertainty”, Tr. SPIIRAN, 23 (2012), 254–295

Citation in format AMSBIB
\Bibitem{Fil12}
\by A.~A.~Fil'chenkov
\paper Measures of truth and probabilistic graphical models for representation of knowledge with uncertainty
\jour Tr. SPIIRAN
\yr 2012
\vol 23
\pages 254--295
\mathnet{http://mi.mathnet.ru/trspy561}
\elib{https://elibrary.ru/item.asp?id=18263552}


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    Citing articles on Google Scholar: Russian citations, English citations
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    This publication is cited in the following articles:
    1. V. F. Musina, “Baiesovskie seti doveriya kak veroyatnostnaya graficheskaya model dlya otsenki meditsinskikh riskov”, Tr. SPIIRAN, 24 (2013), 135–151  mathnet
    2. A. V. Suvorova, “Gibridnye modeli otsenki parametrov sotsialno-znachimogo povedeniya po sverkhmaloi nepolnoi sovokupnosti nablyudenii”, Tr. SPIIRAN, 24 (2013), 116–134  mathnet
    3. A. A. Filchenkov, V. F. Musina, A. L. Tulupev, “Algoritm randomizirovannogo sinteza minimalnogo grafa smezhnosti”, Tr. SPIIRAN, 25 (2013), 221–234  mathnet
    4. V. F. Musina, “Baiesovskie seti doveriya kak veroyatnostnaya graficheskaya model dlya otsenki ekonomicheskikh riskov”, Tr. SPIIRAN, 25 (2013), 235–254  mathnet
    5. A. A. Filchenkov, K. V. Frolenkov, A. V. Sirotkin, A. L. Tulupev, “Sistema algoritmov sinteza podmnozhestv minimalnykh grafov smezhnosti”, Tr. SPIIRAN, 27 (2013), 200–244  mathnet
    6. A. A. Filchenkov, “Preobrazovanie pervichnoi struktury algebraicheskoi baiesovskoi seti k atsiklicheskoi s sokhraneniem veroyatnostnoi semantiki”, Tr. SPIIRAN, 30 (2013), 156–168  mathnet
    7. A. A. Azarov, E. V. Brodovskaya, A. V. Vakhromeeva, A. A. Glazkov, O. V. Dmitrieva, V. F. Musina, V. D. Nechaev, A. V. Suvorova, A. L. Tulupev, T. V. Tulupeva, A. A. Filchenkov, V. I. Shulzhenok, R. M. Yusupov, “Memy i sotsioinzhenernye ataki v virtualnom prostranstve”, Tr. SPIIRAN, 30 (2013), 88–121  mathnet
    8. V. V. Borisov, A. S. Ponomarenko, A. S. Fedulov, “Reshenie problemy nakopleniya neopredelennosti nechetko-veroyatnostnogo baiesovskogo vyvoda”, Nechetkie sistemy i myagkie vychisleniya, 14:2 (2019), 81–91  mathnet  crossref  elib
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