Learning Decomposable Model-Structures by Applying Graphs of Minimal Connectors

S.-H. Kim and R.M. Kil (Korea)


Bayes Network, Conditional Independence, Combined Structure.


In AI and statistics, Bayesian networks and graphical models are very popular tools for modelling knowledge structures. Since model-learning is NP-hard, we need a new approach for learn ing a large model. We propose a method for learning the struc ture of a very large graphical model under the assumption that the models are decomposable.

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