Abstract
Bayesian network (BN) is one of the most classical probabilistic graphical models. It has been widely used in many areas, such as artificial intelligence, pattern recognition, and image processing. Parameter learning in Bayesian network is a very important topic. In this study, six typical parameter learning algorithms were investigated. For the completeness of dataset, there are mainly two categories of methods for parameter estimation in BN: one is suitable to deal with the complete data, and another is for incomplete data. We mainly focused on two algorithms in the first category: maximum likelihood estimate, and Bayesian method; Expectation - Maximization algorithm, Robust Bayesian estimate, Monte - Carlo method, and Gaussian approximation method were discussed for the second category. In the experiment, all these algorithms were applied on a classic example to implement the inference of parameters. The simulating results reveal the inherent differences of these six methods and the effects of the inferred parameters of network on further probability calculation. This study provides insight into the parameter inference strategies of Bayesian network and their applications in different kinds of situations.
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References
Shafer, G.: Probabilistic reasoning in intelligent systems - networks of plausible inference - Pearl. J. Synthese 104(1), 161–176 (1995)
Liao, W.H., Ji, Q.: Learning Bayesian network parameters under incomplete data with domain knowledge. Pattern Recogn. 42(11), 3046–3056 (2009)
Pernkopf, F., Wohlmayr, M.: Stochastic margin-based structure learning of Bayesian network classifiers. Pattern Recogn. 46(2), 464–471 (2013)
Liu, Z.K., Liu, Y.H., Cai, B.P., Zheng, C.: An approach for developing diagnostic Bayesian network based on operation procedures. Expert Syst. Appl. 42(4), 1917–1926 (2015)
Hanninen, M., Banda, O.A.V., Kujala, P.: Bayesian network model of maritime safety management. Expert Syst. Appl. 41(17), 7837–7846 (2014)
De Campos, L.M., Fernandez-Luna, J.M., Huete, J.F.: Bayesian networks and information retrieval: an introduction to the special issue. Inform. Process. Manag. 40(5), 727–733 (2004)
Suojanen, M., Olesen, K.G., Andreassen, S.: A method for diagnosing in large medical expert systems based on causal probabilistic networks. In: Keravnou, T., Baud, R., Garbay, C., Wyatt, J. (eds.) AIME 1997. LNCS, vol. 1211. Springer, Heidelberg (1997)
Ramoni, M., Sebastiani, P.: Robust learning with missing data. Mach. Learn. 45(2), 147–170 (2001)
Furlotte, N.A., Heckerman, D., Lippert, C.: Quantifying the uncertainty in heritability. J. Hum. Genet. 59(5), 269–275 (2014)
Lauritzen, S.L.: The Em algorithm for graphical association models with missing data. Comput. Stat. Data An. 19(2), 191–201 (1995)
Niu, D.X., Shi, H.F., Wu, D.D.: Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm. Appl. Soft Comput. 12(6), 1822–1827 (2012)
Titterington, D.M.: Bayesian methods for neural networks and related models. Stat. Sci. 19(1), 128–139 (2004)
Russell, S., Norvig, P.: Articial Intelligence: A Modern Approach, p. 139 (1995)
Russell, S., Norvig, P.: Articial Intelligence: A Modern Approach (1995)
Acknowledgement
This work was supported by the Zhejiang Provincial Education Department Foundation of China (Grant No. Y201432242) and Talent Start Foundation of Zhejiang A&F University (Grant No. 2009FR061). This work was also partially supported by NSFC No. 61133010.
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Ji, Z., Xia, Q., Meng, G. (2015). A Review of Parameter Learning Methods in Bayesian Network. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_1
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DOI: https://doi.org/10.1007/978-3-319-22053-6_1
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