Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-40897-7_14
Title: A dynamic programming algorithm for learning chain event graphs
Authors: Silander, T.
Leong, T.-Y. 
Keywords: chain event graphs
model selection
structure learning
Issue Date: 2013
Citation: Silander, T.,Leong, T.-Y. (2013). A dynamic programming algorithm for learning chain event graphs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8140 LNAI : 201-216. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-40897-7_14
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. © 2013 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/77952
ISBN: 9783642408960
ISSN: 16113349
DOI: 10.1007/978-3-642-40897-7_14
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