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Bayesian analysis of Markov Modulated Bernoulli Processes

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  • S. Özekici
  • R. Soyer

Abstract

We consider Markov Modulated Bernoulli Processes (MMBP) where the success probability of a Bernoulli process evolves over time according to a Markov chain. The MMBP is applied in reliability modeling where systems and components function in a randomly changing environment. Some of these applications include, but are not limited to, reliability assessment in power systems that are subject to fluctuating weather conditions over time and reliability growth processes that are subject to design changes over time. We develop a general setup for analysis of MMBPs with a focus on reliability modeling and present Bayesian analysis of failure data and illustrate how reliability predictions can be obtained. Copyright Springer-Verlag Berlin Heidelberg 2003

Suggested Citation

  • S. Özekici & R. Soyer, 2003. "Bayesian analysis of Markov Modulated Bernoulli Processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 57(1), pages 125-140, April.
    • Handle: RePEc:spr:mathme:v:57:y:2003:i:1:p:125-140
    DOI: 10.1007/s001860200268
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    Cited by:

    1. S. Özekici & R. Soyer, 2006. "Semi-Markov modulated Poisson process: probabilistic and statistical analysis," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 64(1), pages 125-144, August.
    2. Brian Tomlin, 2009. "Impact of Supply Learning When Suppliers Are Unreliable," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 192-209, August.
    3. Juraj Smiesko & Martin Kontsek & Katarina Bachrata, 2023. "Markov-Modulated On–Off Processes in IP Traffic Modeling," Mathematics, MDPI, vol. 11(14), pages 1-29, July.

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