IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v42y1994i1p137-157.html
   My bibliography  Save this article

Likelihood Ratio Sensitivity Analysis for Markovian Models of Highly Dependable Systems

Author

Listed:
  • Marvin K. Nakayama

    (IBM T. J. Watson Research Center, Yorktown Heights, New York)

  • Ambuj Goyal

    (Rutgers University, Newark, New Jersey)

  • Peter W. Glynn

    (Stanford University, Stanford, California)

Abstract

This paper discusses the application of the likelihood ratio gradient estimator to simulations of large Markovian models of highly dependable systems. Extensive empirical work, as well as some mathematical analysis of small dependability models, suggests that (in this model setting) the gradient estimators are not significantly more noisy than the estimates of the performance measures themselves. The paper also discusses implementation issues associated with likelihood ratio gradient estimation, as well as some theoretical complements associated with application of the technique to continuous-time Markov chains.

Suggested Citation

  • Marvin K. Nakayama & Ambuj Goyal & Peter W. Glynn, 1994. "Likelihood Ratio Sensitivity Analysis for Markovian Models of Highly Dependable Systems," Operations Research, INFORMS, vol. 42(1), pages 137-157, February.
    • Handle: RePEc:inm:oropre:v:42:y:1994:i:1:p:137-157
    DOI: 10.1287/opre.42.1.137
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.42.1.137
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.42.1.137?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
    ---><---

    Citations

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


    Cited by:

    1. Hachicha, Wafik & Ammeri, Ahmed & Masmoudi, Faouzi & Chachoub, Habib, 2010. "A comprehensive literature classification of simulation optimisation methods," MPRA Paper 27652, University Library of Munich, Germany.
    2. Marvin K. Nakayama & Perwez Shahabuddin, 1998. "Likelihood Ratio Derivative Estimation for Finite-Time Performance Measures in Generalized Semi-Markov Processes," Management Science, INFORMS, vol. 44(10), pages 1426-1441, October.
    3. Georgios Arampatzis & Markos A Katsoulakis & Yannis Pantazis, 2015. "Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-24, July.
    4. Marvin K. Nakayama, 1998. "On Derivative Estimation of the Mean Time to Failure in Simulations of Highly Reliable Markovian Systems," Operations Research, INFORMS, vol. 46(2), pages 285-290, April.

    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:inm:oropre:v:42:y:1994:i:1:p:137-157. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.