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Higher-Order 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

Data sequences or time series occur frequently in many real world applications. One of the most important steps in analyzing a data sequence (or time series) is the selection of an appropriate mathematical model for the data. This is because it helps in predictions, hypothesis testing and rule discovery.

Suggested Citation

  • Wai-Ki Ching & Ximin Huang & Michael K. Ng & Tak-Kuen Siu, 2013. "Higher-Order Markov Chains," International Series in Operations Research & Management Science, in: Markov Chains, edition 2, chapter 0, pages 141-176, Springer.
    • Handle: RePEc:spr:isochp:978-1-4614-6312-2_6
    DOI: 10.1007/978-1-4614-6312-2_6
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    Cited by:

    1. Yousif Alyousifi & Kamarulzaman Ibrahim & Mahmod Othamn & Wan Zawiah Wan Zin & Nicolas Vergne & Abdullah Al-Yaari, 2022. "Bayesian Information Criterion for Fitting the Optimum Order of Markov Chain Models: Methodology and Application to Air Pollution Data," Mathematics, MDPI, vol. 10(13), pages 1-16, June.
    2. Pavlos Delias & Vassilios Zoumpoulidis & Ioannis Kazanidis, 2019. "Visualizing and exploring event databases: a methodology to benefit from process analytics," Operational Research, Springer, vol. 19(4), pages 887-908, December.

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