Study on metric decomposition for DBN structure learning

Some correlative properties on dynamic Bayesian networks (DBN) structure metric decomposition for DBN structure learning are proposed. Firstly, DBN's Bayesian information matric (BIC) and Bayesian-Dirichlet metric (BD) decomposition formula are further divided into two parts. Some characters are discussed based on the decomposition formula, and more useful properties are developed. Secondly, a simulation model is designed to verified properties. The properties include two problems, one is the transplantation problem that many static state Bayesian networks (BN) structure learning algorithm can be used to DBN structure learning, the other is computation complexity problem that DBN structure learning time can be lower through DBN structure decomposition. In the end, a good idea is presented for finding a faster and efficient DBN structure learning algorithm.