GENERATIONS IN BAYESIAN NETWORKS
Alexander Litvinenko
litvinenko@uq.rwth-aachen.deChair 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 generationReferences
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
Authors
Alexander Litvinenkolitvinenko@uq.rwth-aachen.de
Chair of Mathematics for Uncertainty Quantification, RWTH Aachen Germany
http://orcid.org/0000-0001-5427-3598
Authors
Natalya LitvinenkoInstitute of Information and Computational Technologies Kazakhstan
http://orcid.org/0000-0002-0576-8305
Authors
Orken MamyrbayevInstitute of Information and Computational Technologies Kazakhstan
http://orcid.org/0000-0001-8318-3794
Authors
Assem ShayakhmetovaInstitute of Information and Computational Technologies Kazakhstan
http://orcid.org/0000-0002-4072-3671
Statistics
Abstract views: 275PDF downloads: 244
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Roman Kvуetnyy, Yuriy Bunyak, Olga Sofina, Oleksandr Kaduk, Orken Mamyrbayev, Vladyslav Baklaiev, Bakhyt Yeraliyeva, ADVERTISING BIDDING OPTIMIZATION BY TARGETING BASED ON SELF-LEARNING DATABASE , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 4 (2023)
- Liudmyla Shkilniak, Waldemar Wójcik, Sergii Pavlov, Oleg Vlasenko, Tetiana Kanishyna, Irina Khomyuk, Oleh Bezverkhyi, Sofia Dembitska, Orken Mamyrbayev, Aigul Iskakova, EXPERT FUZZY SYSTEMS FOR EVALUATION OF INTENSITY OF REACTIVE EDEMA OF SOFT TISSUES IN PATIENTS WITH DIABETES , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 12 No. 3 (2022)
- Veronika Cherkashina, Svitlana Litvinchuk, Vladyslav Lesko, Svetlana Kravets, Volodymyr Netrebskiy, Olena Sikorska, Orken Mamyrbayev, Baglan Imanbek , STUDY OF THE ELECTROMAGNETIC IMPACT OF THE OVERHEAD TRANSMISSION LINES OF 330 KV ON ECOLOGICAL SYSTEMS , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 12 No. 2 (2022)
- Oleg Avrunin, Yana Nosova, Nataliia Shushliapina, Ibrahim Younouss Abdelhamid, Oleksandr Avrunin, Svetlana Kyrylashchuk, Olha Moskovchuk, Orken Mamyrbayev, ANALYSIS OF UPPER RESPIRATORY TRACT SEGMENTATION FEATURES TO DETERMINE NASAL CONDUCTANCE , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 12 No. 4 (2022)
- Waldemar Wójcik, Assem Shayakhmetova, Ardak Akhmetova, Assel Abdildayeva, Galymzhan Nurtugan, OPTIMIZING TIME SERIES FORECASTING: LEVERAGING MACHINE LEARNING MODELS FOR ENHANCED PREDICTIVE ACCURACY , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 14 No. 4 (2024)