Learning dynamic Bayesian network structure with transfer variables
Shuangcheng Wang · Kongzhi yu juece · 2010
At present,there are not the methods of learning dynamic Bayesian network structure from no time symmetry data.Therefore,a method of learning dynamic Bayesian network structure from no time symmetry data is developed by combining transfer variables.In this method,first transfer variable series between two adjacent time slices are found.Then dynamic Bayesian network part structure can be learned based on sorting nodes and local search and scoring method.Finally, a whole dynamic Bayesian network structure can be presented by extending along time series.