Abstract
When adopting Bayesian network (BN) to represent and infer probabilistic causalities among multidimensional variables, the size of the conditional probability table (CPT) associated with each variable is doomed to be large, and the causality inferences cannot be done for arbitrary evidences. In this paper, we first extend the general BN by augmenting parameters for describing causalities among classes instead of specific instances of multidimensional variables. In the extended BN, called CBN, the CPT of a variable includes the probability of each class given parent classes, while a classifier of each variable is associated to determine the class that the given evidence belongs to. Further, we give the method for approximate inferences of the CBN for arbitrary evidences. Preliminary experiments verify the feasibility of our methods.
This work was supported by the National Natural Science Foundation of China (No. 60763007), the Natural Science Foundation of Yunnan Province (No. 2008CD083) and the Research Foundation of the Educational Department of Yunnan Province (No. 08Y0023).
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Yue, K., Wei, MJ., Tian, KL., Liu, WY. (2009). Representing and Inferring Causalities among Classes of Multidimensional Data. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_21
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DOI: https://doi.org/10.1007/978-3-642-00672-2_21
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