Abstract
Chain event graphs are a model family particularly suited for asymmetric causal discrete domains. This paper describes a dynamic programming algorithm for exact learning of chain event graphs from multivariate data. While the exact algorithm is slow, it allows reasonably fast approximations and provides clues for implementing more scalable heuristic algorithms.
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Silander, T., Leong, TY. (2013). A Dynamic Programming Algorithm for Learning Chain Event Graphs. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds) Discovery Science. DS 2013. Lecture Notes in Computer Science(), vol 8140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40897-7_14
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DOI: https://doi.org/10.1007/978-3-642-40897-7_14
Publisher Name: Springer, Berlin, Heidelberg
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