GENERATIONS IN BAYESIAN NETWORKS

Alexander Litvinenko

litvinenko@uq.rwth-aachen.de
Chair of Mathematics for Uncertainty Quantification, RWTH Aachen (Germany)
http://orcid.org/0000-0001-5427-3598

Natalya Litvinenko


Institute of Information and Computational Technologies (Kazakhstan)
http://orcid.org/0000-0002-0576-8305

Orken Mamyrbayev


Institute of Information and Computational Technologies (Kazakhstan)
http://orcid.org/0000-0001-8318-3794

Assem Shayakhmetova


Institute of Information and Computational Technologies (Kazakhstan)
http://orcid.org/0000-0002-4072-3671

Abstract

This paper focuses on the study of some aspects of the theory of oriented graphs in Bayesian networks. In some papers on the theory of Bayesian networks, the concept of “Generation of vertices” denotes a certain set of vertices with many parents belonging to previous generations. Terminology for this concept, in our opinion, has not yet fully developed. The concept of “Generation” in some cases makes it easier to solve some problems in Bayesian networks and to build simpler algorithms. 

In this paper we will consider the well-known example “Asia”, described in many articles and books, as well as in the technical documentation for various toolboxes. For the construction of this example, we have used evaluation versions of AgenaRisk.


Keywords:

Bayesian networks, AgenaRisk, oriented graphs, vertices generation

AgenaRisk 7.0 User Manual. 2016.
  Google Scholar

Bidyuk P., Terentyev A.: Construction and methods of learning of Bayesian Networks. Tavricheskiy vestnik informatiki i matematiki 2/2004, 139–154.
  Google Scholar

Getting Started with AgenaRisk. 2013.
  Google Scholar

http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/020217.pdf (available 15.05.2019).
  Google Scholar

http://www.agenarisk.com (available 15.05.2019).
  Google Scholar

http://www.businessdataanalytics.ru/download/BayesianNetworks.pdf (available 15.05.2019).
  Google Scholar

http://www.cs.technion.ac.il/~dang/books/Learning%20Bayesian%20Networks(Neapolitan,%20Richard).pdf (available 15.05.2019).
  Google Scholar

http://www.stat.yale.edu/~jtc5/BioinformaticsCourse2001/MurphyBayesNetIntro.pdf (available 15.05.2019).
  Google Scholar

https://pdfs.semanticscholar.org/7bc7/54bc548f32b9ac53df67e3171e8e4df66d15.pdf (available 15.05.2019).
  Google Scholar

Jensen F. V., Nielsen T. D.: Bayesian Networks and Decision Graphs. Springer, 2007.
DOI: https://doi.org/10.1007/978-0-387-68282-2   Google Scholar

Litvinenko N., Litvinenko A., Mamyrbayev O., Shayakhmetova A.: On the issue of classification of types of evidence in Bayesian networks. IPIC, Almaty 2018.
  Google Scholar

Litvinenko N., Litvinenko A., Mamyrbayev O., Shayakhmetova A.: Work with Bayesian Networks in BAYESIALAB. IPIC, Almaty 2018.
  Google Scholar

Murphy K. P.: Machine Learning A Probabilistic Perspective. MIT Press, 2012.
  Google Scholar

Download


Published
2019-09-26

Cited by

Litvinenko, A., Litvinenko, N., Mamyrbayev, O., & Shayakhmetova, A. (2019). GENERATIONS IN BAYESIAN NETWORKS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 9(3), 10–13. https://doi.org/10.35784/iapgos.228

Authors

Alexander Litvinenko 
litvinenko@uq.rwth-aachen.de
Chair of Mathematics for Uncertainty Quantification, RWTH Aachen Germany
http://orcid.org/0000-0001-5427-3598

Authors

Natalya Litvinenko 

Institute of Information and Computational Technologies Kazakhstan
http://orcid.org/0000-0002-0576-8305

Authors

Orken Mamyrbayev 

Institute of Information and Computational Technologies Kazakhstan
http://orcid.org/0000-0001-8318-3794

Authors

Assem Shayakhmetova 

Institute of Information and Computational Technologies Kazakhstan
http://orcid.org/0000-0002-4072-3671

Statistics

Abstract views: 234
PDF downloads: 211


Most read articles by the same author(s)