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Author/Affiliation: Benjamin RosmanOpen AccessArticle
8 Pages, 279 KB 
Learning the Influence between Partially Observable Processes using Scorebased Structure Learning
Abstract:
The difficulty of learning the underlying structure between processes is a common task found throughout the sciences, however not much work is dedicated towards this problem. In this paper, we attempt to use the language of structure learning to address learning the dynamic influence network between partially observable processes represented as dynamic Bayesian networks. The…
Read More(This article belongs to the SP10 (Special Issue on Multidisciplinary Sciences and Engineering 2020-21) & Section Artificial Intelligence in Computer Science (CAI))