The methods of constructing dynamic Bayesian networks

This study proposes a method of constructing dynamic Bayesian networks based on statistical data. It consists of two phases: building a static network structure and dynamic structure of the network, which determines the relations between two neighboring time intervals. The structure obtained is used to form the output at each time interval. Finally, we illustrate how this method can be applied to actual data

Publication year: 
2010
Issue: 
2
УДК: 
62-50
С. 55—62, укр., Fig. 7. Tabl. 4. Refs.: 6 titles
References: 

1. Pearl J. Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning (UCLA Technical Report CSD-850017) // 7th Conference of the Cognitive Science Society, University of California, Irvine, CA. — 2009. — Р. 329—334.
2. Montesano L., Lopes M., Bernardino A., Jose Santos-Victor. Modeling Affordances using Bayesian networks // IEEE/ RSJ International Conference on Intelligent Robots and Systems. — San Diego, USA, 2007. — P. 12.
3. Maragoudakis M., Tselios N.K., Fakotakis N., Avouris N.M. Improving SMS usability using Bayesian Networks // Wire Communications Laboratory, Technical Report, 2005. — P. 45.
4. Madden M.G. A New Bayesian Network Structure for Classification Tasks. — Berlin: Springer, 2002. — Р. 183— 197.
5. Бидюк П.И., Терентьев А.Н. Построение и методы обучения байесовских сетей // Тавр. вест. информатики и математики. — Симферополь: КНЦ НАНУ, 2004. — № 2. — С. 139—153.
6. http://www.informatik.uniaugsburg.de/en/chairs/sik/research/finished/ailtbenchmarks/.

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