Parameter Estimation Problems in Markov Random Processes

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review


Problems of convergence and stability of Bayesian estimates in the identification of stochastic control systems are considered. The informational measure of the mismatch between the estimated distribution and the estimate is the main apparatus for establishing the fact of convergence. The choice of a priori distribution of parameters is not always obvious. The Kullback-Leibler information number is taken as such measure. The convergence of the estimates of the transition function of the process to the non-stationary transition function is established in this paper. The problem of synthesis of optimal strategies for dynamic systems in which there is no part of the main information needed for constructing the optimal control is also considered. It is assumed that the system contains at least one unknown parameter belonging to some parameter space. Therefore, the class of control systems considered in the article is the class of parametric adaptive systems.

Original languageEnglish
Title of host publicationComputational Science and Its Applications- ICCSA 2019 - 19th International Conference, Proceedings
EditorsBeniamino Murgante, Osvaldo Gervasi, Elena Stankova, Vladimir Korkhov, Sanjay Misra, Carmelo Torre, Eufemia Tarantino, David Taniar, Ana Maria A.C. Rocha, Bernady O. Apduhan
PublisherSpringer Nature
Number of pages11
ISBN (Print)9783030243043
StatePublished - 1 Jan 2019
Event19th International Conference on Computational Science and Its Applications, ICCSA 2019 - Saint Petersburg, Russian Federation
Duration: 1 Jul 20194 Jul 2019
Conference number: 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11622 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Computational Science and Its Applications, ICCSA 2019
Abbreviated titleICCSA 2019
CountryRussian Federation
CitySaint Petersburg

Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


  • Bayesian probability theory
  • Kullback-Leibler information number
  • Parameter estimation

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