Abstract
An optimization algorithm for dynamic Bayesian networks (DBN) based on Bayesian optimization algorithm (BOA) is developed for learning and constructing the DBN structure. In this paper, we first introduce some basic theories and concepts of probability model evolutionary algorithm. Then we describe, the basic mode for constructing DBN diagram and the mechanism of DBN structure learning based on BOA. The DBN structure learning based on BOA consists of two parts. The first part is to obtain the structure and parameters of DBN in terms of a good solution, and the second part is to produce new groups according to the obtained DBN structure. In this paper, the DBN learning is achieved by genetics algorithm based on a greedy mechanism. The DBN inference is performed by a forward-simulation algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
Supported by NSFC (Grant No. 60404011 and No.60634030).
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Gao, S., Xiao, Q., Pan, Q., Li, Q. (2007). Learning Dynamic Bayesian Networks Structure Based on Bayesian Optimization Algorithm. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_51
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DOI: https://doi.org/10.1007/978-3-540-72393-6_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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