IDEAS home Printed from https://ideas.repec.org/a/spr/mathme/v57y2003i1p125-140.html
   My bibliography  Save this article

Bayesian analysis of Markov Modulated Bernoulli Processes

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s001860200268
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s001860200268?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Brian Tomlin, 2009. "Impact of Supply Learning When Suppliers Are Unreliable," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 192-209, August.
    2. 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.
    3. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:mathme:v:57:y:2003:i:1:p:125-140. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.