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Bayesian networks have many practical applications due to their capability to represent joint probability distribution in many variables in a compact way. Though there exist many algorithms for learning Bayesian networks from data, they are not satisfactory because the learned networks usually are not suitable for reasoning. So far only a restricted class of very simple Bayesian networks: trees and poly-trees are directly applicable in reasoning. This paper defines and explores a new class of networks: the Structured Bayesian Networks. Two methods of reasoning are outlined for this type of networks. Possible methods of learning from data are indicated. Similarity to hiearachical networks is pointed at.
Słowa kluczowe
Rocznik
Tom
Strony
5--25
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
autor
- Instytut Informatyki, Akademia Podlaska
Bibliografia
- [Cercone et al., 1997] Cercone N., Wong S.K.M., Xiang Y.: A 'microscopic' study of minimum entropy search in learning decomposable Markov networks. Machine Learning, 1997, vol. 26, nr 1, 65-92.
- [Cheng et al., 1997] Cheng J., Bell D.A. and Liu W.: Learning belief networks from data: an information theory based approach. Proceedings of the Sixth ACM Int. Conf. on Information and Knowledge Management, 1997.
- [Chow, Liu, 1968] C.K.Chow, C.N.Liu: Approximating discrete probability distributions with dependence trees, IEEE Transactions on Information Theory, Vol. IT-14, No. 3, (1968), 462-467.
- [Cooper, 1990] Cooper G.: The computational complexity of probabilistic inference using Bayesian belief networks. Artif. Intell. 42, 1990, 395-405.
- [Geiger et al., 1990] Geiger D., Verma T., Pearl J.: D-Separation: From theorems to algorithms. M.Henrion, et.al eds. Uncertainty in Artificial Intelligence 5, Elsevier 139-148.
- [Jensen, 1996] Jensen J.: An Introduction to Bayesian Networks. Springer Verlag, 1996. Reasoning Methods in General and Structured Bayesian Networks 25
- [Klopotek, 1999] Klopotek M.A.: Partial Dependency Separation a new concept for expressing dependence/ independence relations in causal networks. Demonstratio Mathematica. Vol XXXII No 1. 1999, pp. 207-226.
- [Klopotek, 2000] Klopotek M.A.: On a Deficiency of the FCI Algorithm Learning Bayesian Networks from Data. Demonstratio Mathematica. Vol. XXXIII, 2000. No. 1, pp. 181-194.
- [Klopotek, 2000b] Klopotek M.A.: Fast Restricted Causal Inference. Demonstratio Mathematica, Vol.XXXIII, No.2, 2000. pp. 419-442.
- [Klopotek, Wierzchon 2000] Klopotek M.A., Wierzchoń S.T.: Partial D-Sepaation for Discovery of Bayesian Networks from Data. R. Trappl, ed.: Cybernetics and Systems. Proc. EMCSR’2000, 25-28.4.2000, Vienna, Austrian Society for Cybernetics, Vol. 2, pp. 707-712.
- [Klopotek, 2001] Klopotek M.A.: Inteligentne wyszukiwarki Internetowe. Akademicka Oficyna Wydawnicza Exit, Warszawa 2001, ISBN 83-8767431-1, chapter 8.9.
- [Klopotek et al., 2001] Klopotek M.A., Wierzchoń S.T., Michalewicz M., Bednarczyk M., Pawłowski W., Wąsowski A.: Bayesian network mining system. M.A. Klopotek, M. Michalewlcz, S.T. Wierzchoń, eds. Intelligent Information Systems 2001. Physica/Springer Verlag, 2001, 180-193.
- [Klopotek, 2002] Klopotek M.A.: Well-Structured Program Graphs and the issue of local computations. Proc. Intelligent Inf. Systems Conf., Sopot, 3-6 June 2002, Physica/Springer Verlag, Heidelberg New York pp. 365-368.
- [Koller, Pfeffer, 1997] Koller D., Pfeffer A.: Object-Oriented Bayesian Networks. Proc. of the 13th Conf. on Uncertainty in Artificial Intelligence (UAI-97), 1997, 302-313.
- [Laurizen, 1988] Lauritzen, S.L., and Spiegelhalter, D.J. Local computations with probabilities on graphical structures and their application to expert systems. J. R. Statist. Soc. B-50(1988)157-224.
- [Pearl, 1988] Pearl J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo CA, 1988.
- [Shachter, 1990] Shachter B.D.: Evidence absorption and propagation through evidence reversals. In M. Henrion, B.D. Shachter, L.N. Kanal, J.F. Lemmer (eds): Uncertainty in Artificial Intelligence 5, Elsevier Science Publishers B.V (1990), 173-190.
- [Shafer, 1996] Shafer, G.: Probabilistic Expert Systems, SIAM CBMS-NSF Regional Conference Series in Applied Mathematics, Philadelphia, 1996, vol. 67.
- [Spirtes et al., 1993] Spirtes P., Glymour C., Scheines R.: Causation, Prediction and Search, Lecture Notes in Statistics 81, Springer-Verlag, 1993.
- [Wierzchon, 1996] Wierzchoń S.T.: Methods of Representation and rocessing of Uncertauin Information in the Dempster-shafer Theory (In Polish), Publisher: Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, 1996.
- [Wierzchon, Klopotek, 2002] Wierzchoń S.T., Kłopotek M.A.: Evidential Reasoning. An Interpretative Investigation. Wydawnictwo Akademii Podlaskiej. Siedlce, 2002 PL ISSN 0860-2719, 304 pages.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4941bf71-ff31-435f-9ae5-7f3f55b242ff