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Book Chapter: On Computation with Higher-order Markov Chains
Title | On Computation with Higher-order Markov Chains |
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Authors | |
Issue Date | 2005 |
Publisher | Springer. |
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? |
Abstract | Categorical 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 Identifier | http://hdl.handle.net/10722/123599 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Ching, WK | en_HK |
dc.contributor.author | Ng, KP | en_HK |
dc.contributor.author | Zhang, S | en_HK |
dc.date.accessioned | 2010-09-26T12:15:16Z | - |
dc.date.available | 2010-09-26T12:15:16Z | - |
dc.date.issued | 2005 | en_HK |
dc.identifier.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 | en_HK |
dc.identifier.isbn | 9783540257851 | - |
dc.identifier.uri | http://hdl.handle.net/10722/123599 | - |
dc.description.abstract | Categorical 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.language | eng | en_HK |
dc.publisher | Springer. | en_HK |
dc.relation.ispartof | 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 | en_HK |
dc.title | On Computation with Higher-order Markov Chains | en_HK |
dc.type | Book_Chapter | en_HK |
dc.identifier.email | Ching, WK: wching@HKUCC.hku.hk | en_HK |
dc.identifier.email | Ng, KP: kkpong@hkusua.hku.hk | en_HK |
dc.identifier.authority | Ching, WK=rp00679 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/3-540-27912-1_2 | - |
dc.identifier.hkuros | 97998 | en_HK |
dc.identifier.spage | 15 | en_HK |
dc.identifier.epage | 24 | en_HK |
dc.publisher.place | Berlin | - |