IDEAS home Printed from https://ideas.repec.org/a/spr/mathme/v46y1997i2p241-250.html
   My bibliography  Save this article

On confidence intervals from simulation of finite Markov chains

Author

Listed:
  • Apostolos Burnetas
  • Michael Katehakis

Abstract

Consider a finite state irreducible Markov reward chain. It is shown that there exist simulation estimates and confidence intervals for the expected first passage times and rewards as well as the expected average reward, with 100% coverage probability. The length of the confidence intervals converges to zero with probability one as the sample size increases; it also satisfies a large deviations property. Copyright Physica-Verlag 1997

Suggested Citation

  • Apostolos Burnetas & Michael Katehakis, 1997. "On confidence intervals from simulation of finite Markov chains," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 46(2), pages 241-250, June.
    • Handle: RePEc:spr:mathme:v:46:y:1997:i:2:p:241-250
    DOI: 10.1007/BF01217693
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF01217693
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF01217693?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.

    References listed on IDEAS

    as
    1. Paul Glasserman & Tai-Wen Liu, 1996. "Rare-Event Simulation for Multistage Production-Inventory Systems," Management Science, INFORMS, vol. 42(9), pages 1292-1307, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel R. Jiang & Warren B. Powell, 2018. "Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures," Mathematics of Operations Research, INFORMS, vol. 43(2), pages 554-579, May.
    2. Paul Glasserman & Yashan Wang, 1999. "Fill-Rate Bottlenecks in Production-Inventory Networks," Manufacturing & Service Operations Management, INFORMS, vol. 1(1), pages 62-76.

    More about this item

    Keywords

    Discrete Markov Chains; Simulation;

    Statistics

    Access and download statistics

    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:46:y:1997:i:2:p:241-250. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.