A Dynamic Programming Algorithm for Learning Chain Event Graphs
Publication Type
Conference Proceeding Article
Publication Date
10-2013
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.
Keywords
chain event graphs, model selection, structure learning
Discipline
Theory and Algorithms
Publication
16th International Conference, Discovery Science 2013, Singapore, October 6-9, 2013. Proceedings
First Page
201
Last Page
216
ISBN
9783642408960
Identifier
10.1007/978-3-642-40897-7_14
Publisher
Springer Verlag
City or Country
Singapore
Citation
Silander T. and Tze-Yun LEONG.
A Dynamic Programming Algorithm for Learning Chain Event Graphs. (2013). 16th International Conference, Discovery Science 2013, Singapore, October 6-9, 2013. Proceedings. 201-216.
Available at: https://ink.library.smu.edu.sg/sis_research/2985