A simulated annealing-based method for learning Bayesian networks from statistical data
Martin Janžura, Jan Nygaard Nielsen · International Journal of Intelligent Systems · 2006
The problem of learning Bayesian networks from statistical data is described and reformulated as a discrete optimization problem. For a solution we employ the stochastic algorithm that is known as simulated annealing and that is based on the Markov Chain Monte Carlo approach. Numerical examples are included to illustrate the efficiency of the method. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 335–348, 2006.