Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Learning equivalence classes of Bayesian network structures
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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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.