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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 |
| Appears in Collections: | Staff Publications |
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