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Multivariate Markov Chains

In: Markov Chains

Author

Listed:
  • Wai-Ki Ching

    (The University of Hong Kong)

  • Ximin Huang

    (Georgia Institute of Technology)

  • Michael K. Ng

    (Hong Kong Baptist University)

  • Tak-Kuen Siu

    (City University London)

Abstract

By making use of the transition probability matrix in, a categorical data sequence of m states can be modeled by an m-state Markov chain model. In this chapter, we extend this idea to model multiple categorical data sequences. One would expect categorical data sequences generated by similar sources or the same source to be correlated to each other. Therefore, by exploring these relationships, one can develop better models for the categorical data sequences and hence better prediction rules.

Suggested Citation

  • Wai-Ki Ching & Ximin Huang & Michael K. Ng & Tak-Kuen Siu, 2013. "Multivariate Markov Chains," International Series in Operations Research & Management Science, in: Markov Chains, edition 2, chapter 0, pages 177-200, Springer.
    • Handle: RePEc:spr:isochp:978-1-4614-6312-2_7
    DOI: 10.1007/978-1-4614-6312-2_7
    as

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