An Algorithm for Bayesian Networks Structure Learning Based on Simulated Annealing
Shao Zhang · 2004
The automated creation of Bayesian networks can be separated into two tasks, Structure learning, which consists of creating the structure of the Bayesian networks from the collected data, and parameter learning, which consists of calculating the numerical parameters for a given structure. We present a simulated annealing algorithm for structure learning in Bayesian networks and propose a score function for optimization based on maximum mutual information entropy with odditional restriction. The entropy is based on KL distance, mutual information and maximum mutual information. We also propose a denotative form for variable and give a mechanism for generating contiguous data. Some experimentation on other simulated annealing and genetic or Evolutionary algorithms are given. The result indicates that the simulated annealing algorithm based MMI-L that we propose has more efficiency and precisely in cost and precision than others.