Learning Graphical Models with Hypertree Structure Using a Simulated Annealing Approach

Abstract — A major topic of recent research in graphical models has been to develop algorithms to learn them from a dataset of sample cases. However, most of these algorithms do not take into account that learned graphical models may be used for time-critical reasoning tasks and that in this case the time complexity of evidence propagation may have to be restricted, even if this can be achieved only by accepting approximations. In this paper we suggest a simulated annealing approach to learn graphical models with hypertree structure, with which the complexity of the popular join tree evidence propagation method can be controlled at learning time by restricting the size of the cliques of the learned network. I