Title :
PCMHS-Based Algorithm for Bayesian Networks Online Structure Learning
Author :
Jun, Xie ; Li, Wang
Author_Institution :
Dept. of Inf. Syst., Beihang Univ., Beijing, China
Abstract :
Given Bayesian Networks online structure learning problem, the paper presents an algorithm based on importance sampling and Parallel Crossover Metropolis-Hasting Sampler for evaluating online samples and network structure learning. The algorithm firstly selects the best samples for online structure learning using importance sampling method, and adjusts them according to the existed reliable network structure. Then on the basis of mutual information among nodes of the network, it initializes several parallel Markov Chains converging to Boltzmann distribution. At last new reliable network structure is formed by evaluating the learned structures in the process of iteration. The experimental result on standard data set shows that the algorithm can achieve online structure adjustment, and meanwhile has a high convergence speed, integration and learning accuracy.
Keywords :
Markov processes; belief networks; data handling; importance sampling; learning (artificial intelligence); Bayesian networks; Boltzmann distribution; PCMHS algorithm; importance sampling method; network structure learning; online structure learning problem; parallel Markov chains; parallel crossover metropolis hasting sampler; Bayesian methods; Boltzmann distribution; Computer networks; Convergence; Iterative algorithms; Machine learning; Machine learning algorithms; Monte Carlo methods; Probability; Sampling methods; BDE; Bayesian Networks; Online Structure Learning; PCMHS;
Conference_Titel :
Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on
Conference_Location :
Chongqing
Print_ISBN :
978-0-7695-3930-0
Electronic_ISBN :
978-1-4244-5423-5
DOI :
10.1109/IFCSTA.2009.316
