Abo Bibliothek: Guest
Digitales Portal Digitale Bibliothek eBooks Zeitschriften Referenzen und Berichte Forschungssammlungen
Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Druckformat: 1064-2315
ISSN Online: 2163-9337

Volumes:
Volumen 51, 2019 Volumen 50, 2018 Volumen 49, 2017 Volumen 48, 2016 Volumen 47, 2015 Volumen 46, 2014 Volumen 45, 2013 Volumen 44, 2012 Volumen 43, 2011 Volumen 42, 2010 Volumen 41, 2009 Volumen 40, 2008 Volumen 39, 2007 Volumen 38, 2006 Volumen 37, 2005 Volumen 36, 2004 Volumen 35, 2003 Volumen 34, 2002 Volumen 33, 2001 Volumen 32, 2000 Volumen 31, 1999 Volumen 30, 1998 Volumen 29, 1997 Volumen 28, 1996

Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v42.i11.40
pages 36-45

Comparative Analysis of Estimation Methods of Vertices Correlation while Bayesian Networks Construction

Petr I. Bidyuk
Institute for Applied System Analysis of National Technical University of Ukraine "Igor Sikorsky Kiev Polytechnic Institute", Kiev
Vladimir I. Davidenko
Joint stock company 'Raiffeisen Bank Aval", Kiev, Ukraine
Dmitriy V. Trofimenko
Educational-scientific complex Institute of Applied System Analysis of National Technical University of Ukraine "Kiev Polytechnic Institute", Ukraine
Alexander N. Terentyev
Institute of Applied System Analysis of National Technical University of Ukraine "Kiev Polytechnical Institute", Kiev, Ukraine

ABSTRAKT

While Bayesian networks construction the estimation methods for the correlation between the vertices are analyzed, using the heuristic algorithm. The theoretical justification of methods is carried out, the results of their practical use while construction of classical networks are considered and the algorithm of carrying out experiments on the basis of pseudo-random generation of Bayesian networks is described. The results, obtained for every method, were compared. The conclusions about the applicability of the considered methods of estimating the correlation between the vertices while constructing Bayesian networks are made.


Articles with similar content:

Information-extreme Algorithm for Recognizing Current Distribution Maps in Magnetocardiography
Journal of Automation and Information Sciences, Vol.43, 2011, issue 2
Alexander S. Kovalenko, Sergey S. Martynenko, Nikolay N. Budnyk, Anatoliy S. Dovbysh
Recognition of Dactylemes of Ukrainian Sign Language Based on the Geometric Characteristics of Hand Contours Defects
Journal of Automation and Information Sciences, Vol.48, 2016, issue 4
Andrey A. Golik , Veda S. Kasianiuk, Yuriy V. Krak
Robust Multiobjective Identification of Nonlinear Objects Based on Evolving Radial Basis Networks
Journal of Automation and Information Sciences, Vol.45, 2013, issue 9
Alexander A. Bezsonov, Oleg G. Rudenko
Modern Approaches to Solving Complex Discrete Optimization Problems
Journal of Automation and Information Sciences, Vol.48, 2016, issue 1
Vladimir P. Shylo , Ivan V. Sergienko
Approach to the Study of Global Asymptotic Stability of Lattice Differential Equations with Delay for Modeling of Immunosensors
Journal of Automation and Information Sciences, Vol.51, 2019, issue 2
Andrey S. Sverstiuk , Igor Ye. Andrushchak, Vasiliy P. Martsenyuk