Exploiting local and repeated structure in Dynamic Bayesian Networks

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• 作者：Jonas Vlasselaer ; ^{jonas.vlasselaer@cs.kuleuven.be" class="auth_mail" title="E-mail the corresponding author} ; Wannes Meert ; Guy Van den Broeck ; Luc De Raedt
• 关键词：Probabilistic graphical models ; Dynamic Bayesian Networks ; Probabilistic inference ; Knowledge compilation
• 刊名：Artificial Intelligence
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：232
• 期：Complete
• 页码：43-53
• 全文大小：642 K

文摘

We introduce the structural interface algorithm for exact probabilistic inference in Dynamic Bayesian Networks. It unifies state-of-the-art techniques for inference in static and dynamic networks, by combining principles of knowledge compilation with the interface algorithm. The resulting algorithm not only exploits the repeated structure in the network, but also the local structure, including determinism, parameter equality and context-specific independence. Empirically, we show that the structural interface algorithm speeds up inference in the presence of local structure, and scales to larger and more complex networks.