- Title
- Polynomial time algorithm for learning globally optimal Dynamic Bayesian network
- Creator
- Nguyen, Vinh; Chetty, Madhu; Coppel, Ross; Wangikar, Prangipar
- Date
- 2011
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/157234
- Identifier
- vital:11556
- Identifier
-
https://doi.org/10.1007/978-3-642-24965-5_81
- Identifier
- ISBN:978-3-642-24964-8
- Abstract
- This paper is concerned with the problem of learning the globally optimal structure of a dynamic Bayesian network (DBN). We propose using a recently introduced information theoretic criterion named MIT (Mutual Information Test) for evaluating the goodness-of-fit of the DBN structure. MIT has been previously shown to be effective for learning static Bayesian network, yielding results competitive to other popular scoring metrics, such as BIC/MDL, K2 and BD, and the well-known constraint-based PC algorithm. This paper adapts MIT to the case of DBN. Using a modified variant of MIT, we show that learning the globally optimal DBN structure can be efficiently achieved in polynomial time.; Lecture Notes in Computer Science, vol 7064.
- Publisher
- Springer
- Relation
- 18th International Conference, ICONIP 2011. Shanghai, China, 13th-17th November, 2011 In Neural Information Processing. ICONIP 2011 (Lecture Notes in Computer Science) vol 7064. p 719-729
- Rights
- Copyright Springer
- Rights
- This metadata is freely available under a CCO license
- Subject
- Dynamic Bayesian network; Global optimization; Gene regulatory network
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