A PSO approach for learning transition structures of Higher-Order Dynamic Bayesian Networks

Dynamic Bayesian Networks are widely used for modeling neural information flow and gene regulatory networks. Their assumption of first-order Markov, however, is recently being noted as a too restrictive assumption for some applications, specially when these have communication and processing of information at different time delays. Many authors are extending this Markov assumption to higher orders, suggesting the use of Higher-Order Dynamic Bayesian Networks (HO-DBNs). These networks, by their turn, bring some issues to be considered, mainly because of the elevated number of nodes, the necessity of great amounts of data and the high cost for learning their structures. In this work we propose an optimization technique based on Particle Swarm Optimization for learning the transition structures of HO-DBNs, trying to exploit their intrinsic characteristics to suggest efficient representations and simplifications in their learning. Also, we propose a way to introduce extra randomness in the method by reinterpreting the concepts of inertia and social/cognitive contributions to particles. We test the algorithm for correctness and performance, comparing it against a greedy algorithm for learning Bayesian structures.