Learning Complex Bayesian Network Features for Classification.
Péter Antal, András Gézsi, Gábor Hullám, András Millinghoffer · 2006
The increasing complexity of the models, the abundant electronic literature and the relative scarcity of the data make it necessary to use the Bayesian approach to complex queries based on prior knowledge and structural models. In the paper we discuss the probabilistic semantics of such statements, the computational challenges and possible solutions of Bayesian inference over complex Bayesian network features, particularly over features relevant in the conditional analysis. We introduce a special feature called Markov Blanket Graph. Next we present an application of the ordering-based Monte Carlo method over Markov Blanket Graphs and Markov Blanket sets. In the Bayesian approach to a structural feature F with values F(G) ∈ {fi} R i=1 we are interested in the feature posterior induced by the model posterior given the observations DN, where G denotes the structure of the Bayesian network (BN)