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Medical Intervention for Disease Stages Using Game Theory, Markov Chains, and Bayesian Inference

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  • Ahmed Merie
  • Myron Hlynka

Abstract

In this paper, we study the progression of disease in the body, using Markov chains. We analyze the problem using game theory, and we use the results to estimate initial probabilities for our transition matrices. We also use Bayesian methods to obtain transition probabilities. We present three examples to explain how this process works.

Suggested Citation

  • Ahmed Merie & Myron Hlynka, 2019. "Medical Intervention for Disease Stages Using Game Theory, Markov Chains, and Bayesian Inference," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 8(4), pages 60-67, July.
    • Handle: RePEc:ibn:ijspjl:v:8:y:2019:i:4:p:60
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    References listed on IDEAS

    as
    1. David Lunn & Jessica Barrett & Michael Sweeting & Simon Thompson, 2013. "Fully Bayesian hierarchical modelling in two stages, with application to meta-analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 551-572, August.
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    More about this item

    Keywords

    cancer; Markov chain; game theory; Bayesian inference;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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