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A decoupling principle for Markov-modulated chains

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  • Qiu, Qinjing
  • Kawai, Reiichiro

Abstract

Markov modulation has been widely employed in various fields of application, such as finance, economics, information and computer sciences, operations research, healthcare, and bio-medicines, whereas the additional modeling flexibility comes at the cost of demanding computation and complex inference procedure. The aim of this paper is to establish a decoupling principle for Markov-modulated chains, which enables one to represent an expectation on a Markov-modulated chain by a convergent sequence written on a set of ordinary continuous-time Markov chains. The proposed decoupling principle covers a large class of Markov-modulated chains, ranging from a variety of Markov-modulated processes to time-inhomogeneous models without time-discretization of the generator matrices, and has great potential for easing the computation around Markov-modulated chains.

Suggested Citation

  • Qiu, Qinjing & Kawai, Reiichiro, 2022. "A decoupling principle for Markov-modulated chains," Statistics & Probability Letters, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:stapro:v:182:y:2022:i:c:s0167715221002637
    DOI: 10.1016/j.spl.2021.109301
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    References listed on IDEAS

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    1. U. Yechiali & P. Naor, 1971. "Queuing Problems with Heterogeneous Arrivals and Service," Operations Research, INFORMS, vol. 19(3), pages 722-734, June.
    2. Jackson, Christopher, 2011. "Multi-State Models for Panel Data: The msm Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i08).
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    Cited by:

    1. Qinjing Qiu & Reiichiro Kawai, 2023. "Iterative Weak Approximation and Hard Bounds for Switching Diffusion," Journal of Theoretical Probability, Springer, vol. 36(2), pages 1003-1036, June.

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