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On Computation with Higher-order Markov Chains

In: Current Trends in High Performance Computing and Its Applications

Author

Listed:
  • 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)

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.

Suggested Citation

  • Waiki Ching & Michael K. Ng & Shuqin Zhang, 2005. "On Computation with Higher-order Markov Chains," Springer Books, in: Wu Zhang & Weiqin Tong & Zhangxin Chen & Roland Glowinski (ed.), Current Trends in High Performance Computing and Its Applications, pages 15-24, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27912-9_2
    DOI: 10.1007/3-540-27912-1_2
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