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Bayesian belief networks represent and process probabilistic knowledge. This representation rigorously describes the knowledge of some domains and it is a human easy-use qualitative structure that facilitates communication between a user and a system incorporating the probabilistic model. Learning Bayesian network from data may be grouped into two modelling situations: qualitative learning and quantitative learning. The first one consists in establishing the structure of the network, whereas the second concerns determining parameters of the network (conditional probabilities). Both modelling methods were applied on exemplary data to show the possibilities and benefits of this methods. The results and conclusions are presented. It was necessary to preprocess the date first. The used method, described in detail in the paper, consists in discretization into linguistic states on the basis of evaluated signal derivative. Some remarks about adjusting the network, as a part of model identification, are also presented.
Słowa kluczowe
Rocznik
Tom
Strony
103--110
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wykr.
Twórcy
autor
- Silesian University of Technology [Politechnika Śląska], Department of Fundamentals of Machine Design, ul. Konarskiego 18 A, 44-100 Gliwice
Bibliografia
- [1] B. Abramson. The design of Behef Network-based Systems for Price Forecasting. Comput. Elect. Eng., 20, 1994.
- [2] S. Andreassen, M. Woldbye, B. Falck, S.K. Andersen. MUNIN: A Casual probabilistic Network for Interpretation of Electromyographic findings. In; Proceedings of the 10-th International Join Conference on Artificial Inteligence, Morgan Kaufmann, San Mateo, Calif., Aug. 1987.
- [3] S. Acida, L.M. de Campos, J.M. Fernandez-Luna, S. Rodriguez, J.M. Rodriguez, J.L. Salcedo: A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service. Artificial Intelligence in Medicine, 30: 215-232, 2004
- [4] M. Bednarski, W. Cholewa, W. Frid. Identification of sensitivities in Bayesian networks. Engineering Applications of Artificial Intelligence, 17: 327-335, 2004
- [5] M. Bednarski, W. Cholewa, W. Frid, M. Galek. Simulation of Nuclear Reactors Accidents. Department of Fundamentals of Machine Design, Zeszyt 124, Gliwice, 2004.
- [6] M. Bednarski, W. Cholewa, W. Frid. The sensitivity analysis of Bayesian Networks. Procedings of the Symposium on Methods of Artificial Intelligence AI-METH 2003, 5-7 November, Gliwice, 2003.
- [7] Belief Network PowerConstructor. http://www.cs.ualberta.ca/jcheng/bnpc.htm.
- [8] J. Breese, E. Horvitz, M. Peot, R. Gay, G. Quentin. Automated Decision-Analytic Diagnosis of Thermal Performance in Gas Turbines. In: Proceedings of the International Gas Turbine and Aeroengine Congress and exposition, American Society of Mechanical Engineers, New York, June 1992.
- [9] E. Charniak. Bayesian Networks without Tears. Al Magazine, 12(4), winter 1991.
- [10] J. Cheng, D.A. Bell, W. Liu. An algorithm for Bayesian belief network construction from data. Proceedings of Al and STAT'97, 1997. p. 83-90.
- [11] E. Grindon et al. A rapid response source term indicator based on plant status for use in emergency response (STERPS). Proceedings of FISA-2003, EU research in reactor safety, 10-13 November 2003.
- [12] D. Heckerman, D. Geiger, D.M. Chickering. Learning Bayesian networks: the combination of knowledge and statistical data. Mach Leam, 20: 197-243 1995.
- [13] Finn V. Jensen. An Introduction to Bayesian Networks. UCL Press and Springer Verlag, 1996.
- [14] Finn V. Jensen. Bayesian Networks and Decision Graphs. Springer-Verlag, 2001.
- [15] M.A. Kłopotek, et al. Bayesian Network Mining System. In: M.A. Kłopotek, M. Michalewicz, S.T. Wierzchoń eds. Advances in Soft Computing, Springer-Verlag, Berlin-Heidelberg 2001, pp. 179-193,
- [16] M.A. Kłopotek, S.T. Wierzchoń. Bayesian Nets for Recommender Systems. In: M.A. Kłopotek, S.T. Wierzchoń, K. Trojanowski, eds. Advances in Soft Computing, Springer-Verlag, Berlin-Heidelberg 2003, pp. 87-96.
- [17] The MathWorks, http://www.mathworks.com/.
- [18] Modular Accident Analysis Program MAAP4: www.maap4.com.
- [19] F. Nadi, A. Agogino, D. Hodges. Use of Influence Diagrams and Neural Networks in Modeling Semiconductor Manufacturing Processes. IEEE Trans, on Semicond. Mfg., 4, 1991.
- [20] Norsys Software Corp. http://www.norsys.com.
- [21] Veerle M.H. Coupe, Linda C. van der Gaag. Properties of sensitivity analysis of Bayesian belief networks. Annals of Mathematics and Artificial Intelligence, 36: 323-356, 2002.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-article-BPB1-0019-0029