Abstract
In real applications established on Bayesian networks (BNs), it is necessary to make inference for arbitrary evidence even it is not contained in existing conditional probability tables (CPTs). Aiming at this problem, in this paper, we discuss the learning and inferences of the BN with maximum likelihood parameters that replace the CPTs. We focus on the learning of the maximum likelihood parameters and give the corresponding methods for 2 kinds of BN inferences: forward inferences and backward inferences. Furthermore, we give the approximate inference method of BNs with maximum likelihood hypotheses. Preliminary experiments show the feasibility of our proposed methods.
This work was supported by the National Natural Science Foundation of China (No. 60763007) and the Cultivation Project for Backbone Teachers of Yunnan University.
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References
Pearl, J.: Probabilistic reasoning in intelligent systems: network of plausible inference. Morgen Kaufmann publishers, Inc., San Mates (1998)
Heckerman, D., Wellman, M.P.: Bayesian networks. Communication of the ACM 38(3), 27–30 (1995)
Russel, S.J., Norving, P.: Artificial intelligence - a modern approach. Pearson Education Inc., Prentice-Hall (2002)
Liu, W.Y., Yue, K., Zhang, J.D.: Augmenting Learning Function to Bayesian Network Inferences with Maximum Likelihood Parameters. Expert Systems with Applications: An International Journal, ESWA 41(2) (to appear, 2008)
Mitchell, T.M.: Machine Learning. McGraw-Hill Companies, Inc, New York (1997)
Cheng, J., Greiner, R., Kelly, J., Bell, D., Liu, W.: Learning Bayesian network from data: an information-theory based approach. Artificial Intelligence 137(2), 43–90 (2002)
Vapnik, V.: Statistical learning theory. John Wiley and Sons, Inc., Chichester (1998)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Platt, J.C.: Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In: Advances in Large Margin Classifiers (1999)
Bergh, J., Lofstrom, J.: Interpolation Spaces an Introduction. Springer, Inc., Heidelberg (1976)
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Zhang, J., Yue, K., Liu, W. (2008). Learning and Inferences of the Bayesian Network with Maximum Likelihood Parameters. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_36
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DOI: https://doi.org/10.1007/978-3-540-88192-6_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88191-9
Online ISBN: 978-3-540-88192-6
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