On Computation with Higher-order Markov Chains

Waiki Ching (), Michael K. Ng () and Shuqin Zhang ()
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Waiki Ching: The University of Hong Kong, Department of Mathematics
Michael K. Ng: The University of Hong Kong, Department of Mathematics
Shuqin Zhang: The University of Hong Kong, Department of Mathematics

A chapter in Current Trends in High Performance Computing and Its Applications, 2005, pp 15-24 from Springer

Abstract: Summary 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.

Keywords: Prediction Accuracy; Linear Programming Problem; Markov Chain Model; Simulated Sequence; Order Markov Chain (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-27912-9_2

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DOI: 10.1007/3-540-27912-1_2

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