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Conditional Monte Carlo

Gradient Estimation and Optimization Applications

  • Book
  • © 1997

Overview

Authors:
  1. Michael Fu

    1. University of Maryland, College Park, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Jian-Qiang Hu

    1. Boston University, Boston, USA

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 392)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xv
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  2. Introduction

    • Michael Fu, Jian-Qiang Hu
    Pages 1-32

  3. Three Extended Examples

    • Michael Fu, Jian-Qiang Hu
    Pages 33-72

  4. Conditional Monte Carlo Gradient Estimation

    • Michael Fu, Jian-Qiang Hu
    Pages 73-131

  5. Links to Other Settings

    • Michael Fu, Jian-Qiang Hu
    Pages 133-180

  6. Synopsis and Preview

    • Michael Fu, Jian-Qiang Hu
    Pages 181-204

  7. Queueing Systems

    • Michael Fu, Jian-Qiang Hu
    Pages 205-284

  8. (s, S) Inventory Systems

    • Michael Fu, Jian-Qiang Hu
    Pages 285-314

  9. Other Applications

    • Michael Fu, Jian-Qiang Hu
    Pages 315-365

  10. Back Matter

    Pages 367-399
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Keywords

About this book

Conditional Monte Carlo: Gradient Estimation and Optimization Applications deals with various gradient estimation techniques of perturbation analysis based on the use of conditional expectation. The primary setting is discrete-event stochastic simulation. This book presents applications to queueing and inventory, and to other diverse areas such as financial derivatives, pricing and statistical quality control. To researchers already in the area, this book offers a unified perspective and adequately summarizes the state of the art. To researchers new to the area, this book offers a more systematic and accessible means of understanding the techniques without having to scour through the immense literature and learn a new set of notation with each paper. To practitioners, this book provides a number of diverse application areas that makes the intuition accessible without having to fully commit to understanding all the theoretical niceties. In sum, the objectives of this monograph are two-fold: to bring together many of the interesting developments in perturbation analysis based on conditioning under a more unified framework, and to illustrate the diversity of applications to which these techniques can be applied.
Conditional Monte Carlo: Gradient Estimation and Optimization Applications is suitable as a secondary text for graduate level courses on stochastic simulations, and as a reference for researchers and practitioners in industry.

Authors and Affiliations

  • University of Maryland, College Park, USA

    Michael Fu

  • Boston University, Boston, USA

    Jian-Qiang Hu

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