Structural Learning of Graphical Models and Its Applications to Traditional Chinese Medicine
Bayesian networks and undirected graphical models are often used to cope with uncertainty for complex systems with a large number of variables. They can be applied to discover causal relationships and associations between variables. In this paper, we present heuristic algorithms for structural learning of undirected graphical models from observed data. These algorithms are applied to traditional Chinese medicine.
KeywordsMutual Information Traditional Chinese Medicine Akaike Information Criterion Bayesian Network Graphical Model
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