Dynamic MMHC: A Local Search Algorithm for Dynamic Bayesian Network Structure Learning

Dynamic Bayesian networks (DBNs) are a class of probabilistic graphical models that has become a standard tool for modeling various stochastic time-varying phenomena. The temporal probabilistic graphical models as 2TBN are the most used and popular models for DBNs. Because of the complexity induced by adding the temporal dimension, DBN structure learning is a very complex task. Existing algorithms are adaptations of score-based BN structure learning algorithms but are often limited when the number of variables is high. We focus in this paper to DBN structure learning with another family of structure learning algorithms, local search methods, known for its scalability. In this paper, we propose Dynamic MMHC, an adaptation of the "static" MMHC algorithm. We illustrate the interest of this method with one toy example and finally give some experimental results.