A Dynamic Programming Algorithm for Learning Chain Event Graphs

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 prov...

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Main Authors: Silander T., Tze-Yun LEONG
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/2985
http://link.springer.com/chapter/10.1007%2F978-3-642-40897-7_14
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spelling sg-smu-ink.sis_research-39852016-02-25T07:43:44Z A Dynamic Programming Algorithm for Learning Chain Event Graphs Silander T., Tze-Yun LEONG, 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. 2013-10-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/2985 info:doi/10.1007/978-3-642-40897-7_14 http://link.springer.com/chapter/10.1007%2F978-3-642-40897-7_14 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University chain event graphs model selection structure learning Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic chain event graphs
model selection
structure learning
Theory and Algorithms
spellingShingle chain event graphs
model selection
structure learning
Theory and Algorithms
Silander T.,
Tze-Yun LEONG,
A Dynamic Programming Algorithm for Learning Chain Event Graphs
description 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.
format text
author Silander T.,
Tze-Yun LEONG,
author_facet Silander T.,
Tze-Yun LEONG,
author_sort Silander T.,
title A Dynamic Programming Algorithm for Learning Chain Event Graphs
title_short A Dynamic Programming Algorithm for Learning Chain Event Graphs
title_full A Dynamic Programming Algorithm for Learning Chain Event Graphs
title_fullStr A Dynamic Programming Algorithm for Learning Chain Event Graphs
title_full_unstemmed A Dynamic Programming Algorithm for Learning Chain Event Graphs
title_sort dynamic programming algorithm for learning chain event graphs
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/2985
http://link.springer.com/chapter/10.1007%2F978-3-642-40897-7_14
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