Learning discrete Bayesian network structures from data:based on kernel variables
Bangzuo Zhang, Hui Wang · Journal of Northeast Normal University · 2005
In this paper,based on kernel variables,the method of learning discrete Bayesian network structures from data was developed.This method is made up of three parts.First,a directed acyclic graph is built in terms of unconditional relative forecasting ability between variables and the variables are sorted degressively according to the convergence degree and divergence degree of variables.Second,two variables,which respectively have maximum convergence degree and divergence degree and are different from forecasted variables,are selected as conditional variables.The existence and direction of arc between two variables are made in terms of conditional relative forecasting ability and an elementary Bayesian network structure is built with checking cyclic route.Third,given conditional set(minimum d-separating set of two variables),the elementary Bayesian network structure is regulated(to increase the lost arcs,to delete superfluous arce and to regulate direction of arcs)in terms of conditional relative forecasting ability and a Bayesian network structure is built with checking cyclic route.In the mean time,a contrast experiment was made by using simulated data.