Abstract
Dynamic Bayesian networks (DBNs) are a class of probabilistic graphical models that has become a standard tool for modeling various stochastic time-varying phenomena. Probabilistic graphical models such as 2-Time slice BN (2T-BNs) 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. We propose Dynamic MMHC, an adaptation of the ”static” MMHC algorithm. We illustrate the interest of this method with some experimental results.
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Trabelsi, G., Leray, P., Ben Ayed, M., Alimi, A.M. (2013). Dynamic MMHC: A Local Search Algorithm for Dynamic Bayesian Network Structure Learning. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_34
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DOI: https://doi.org/10.1007/978-3-642-41398-8_34
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