Abstract
This paper discusses the change for the conditional independence set in learning Probabilistic Network based on markov property. They are generalized into several cases for all of possible changes. We show that these changes are sound and complete. Any structure learning methods for the Decomposiable Markov Network and Bayesian Network will fall into these cases. This study indicates which kind of domain model can be learned and which can not. It suggests that prior knowledge about the problem domain decides the basic frame for the future learning.
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© 2001 Springer-Verlag Berlin Heidelberg
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Lin, T., Huang, Y. (2001). A Study of Conditional Independence Change in Learning Probabilistic Network. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_56
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DOI: https://doi.org/10.1007/3-540-45554-X_56
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