IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v31y2025i2p109-118n1002.html
   My bibliography  Save this article

Bayesian inference of traffic intensity in M/M/1 queue under symmetric and asymmetric loss functions

Author

Listed:
  • Kushvaha Bhaskar

    (Department of Statistics, Dibrugarh University, Dibrugarh, India)

  • Das Dhruba

    (Department of Statistics, Dibrugarh University, Dibrugarh, India)

  • Tamuli Asmita

    (Department of Statistics, Dibrugarh University, Dibrugarh, India)

Abstract

In this article, Bayesian estimators of the traffic intensity (ρ) in single server Markovian ( M / M / 1 {M/M/1} ) queueing system are derived under the squared error loss function (SELF) and precautionary loss function (PLF). These Bayes estimators are derived using three different priors viz. beta, independent gamma and Jeffrey distribution. The effectiveness of the proposed Bayes estimators are compared in terms of their posterior risks. A suitable prior is chosen for Bayesian analysis using the model comparison criterion based on the Bayes factor.

Suggested Citation

  • Kushvaha Bhaskar & Das Dhruba & Tamuli Asmita, 2025. "Bayesian inference of traffic intensity in M/M/1 queue under symmetric and asymmetric loss functions," Monte Carlo Methods and Applications, De Gruyter, vol. 31(2), pages 109-118.
    • Handle: RePEc:bpj:mcmeap:v:31:y:2025:i:2:p:109-118:n:1002
    DOI: 10.1515/mcma-2025-2005
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma-2025-2005
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/mcma-2025-2005?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.

    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:bpj:mcmeap:v:31:y:2025:i:2:p:109-118:n:1002. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.