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Book Chapter: On Computation with Higher-order Markov Chains

TitleOn Computation with Higher-order Markov Chains
Authors
Ching, WKNg, KPZhang, S
Issue Date2005
PublisherSpringer.
Citation
On Computation with Higher-order Markov Chain. In Zhang, W, Tong, W, Chen, Z, et al. (Eds.), Current Trends in High Performance Computing and Its Applications: Proceedings of the International Conference on High Performance Computing and Applications, August 8–10, 2004, Shanghai, P.R. China, p. 15-24. Berlin: Springer, 2005 How to Cite?
DOI: http://dx.doi.org/10.1007/3-540-27912-1_2
AbstractCategorical data sequences occur in many real world applications. The major problem in using higher-order Markov chain model is that the number of parameters increases exponentially with respect to the order of the model. In this paper, we propose a higher-order Markov chain model for modeling categorical data sequences where the number of model parameters increases linearly with respect to the order of the model. We present efficient estimation methods based on linear programming for the model parameters. The model is then compared with other existing models with simulated sequences and DNA data sequences of mouse.
Persistent Identifierhttp://hdl.handle.net/10722/123599
ISBN
9783540257851

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorNg, KPen_HK
dc.contributor.authorZhang, Sen_HK
dc.date.accessioned2010-09-26T12:15:16Z-
dc.date.available2010-09-26T12:15:16Z-
dc.date.issued2005en_HK
dc.identifier.citationOn Computation with Higher-order Markov Chain. In Zhang, W, Tong, W, Chen, Z, et al. (Eds.), Current Trends in High Performance Computing and Its Applications: Proceedings of the International Conference on High Performance Computing and Applications, August 8–10, 2004, Shanghai, P.R. China, p. 15-24. Berlin: Springer, 2005en_HK
dc.identifier.isbn9783540257851-
dc.identifier.urihttp://hdl.handle.net/10722/123599-
dc.description.abstractCategorical data sequences occur in many real world applications. The major problem in using higher-order Markov chain model is that the number of parameters increases exponentially with respect to the order of the model. In this paper, we propose a higher-order Markov chain model for modeling categorical data sequences where the number of model parameters increases linearly with respect to the order of the model. We present efficient estimation methods based on linear programming for the model parameters. The model is then compared with other existing models with simulated sequences and DNA data sequences of mouse.-
dc.languageengen_HK
dc.publisherSpringer.en_HK
dc.relation.ispartofCurrent Trends in High Performance Computing and Its Applications: Proceedings of the International Conference on High Performance Computing and Applications, August 8–10, 2004, Shanghai, P.R. Chinaen_HK
dc.titleOn Computation with Higher-order Markov Chainsen_HK
dc.typeBook_Chapteren_HK
dc.identifier.emailChing, WK: wching@HKUCC.hku.hken_HK
dc.identifier.emailNg, KP: kkpong@hkusua.hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/3-540-27912-1_2-
dc.identifier.hkuros97998en_HK
dc.identifier.spage15en_HK
dc.identifier.epage24en_HK
dc.publisher.placeBerlin-

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