Learning Dynamic Bayesian Network Structure from Non-Time Symmetric Data

Abstract

At present, there are not the methods of learning dynamic Bayesian network structure from no time symmetry data. In this paper, a method of learning dynamic Bayesian network structure from non-time symmetric data is developed by dint of transfer variables. In this method, first transfer variables between two adjacent time slices are learned by combining star structure and Gibbs sampling. Then dynamic Bayesian network part structure can be built based on sorting nodes and local search & scoring method. A complete dynamic Bayesian network structure can be obtained by extending along time series.