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Computer Science - Theoretical Computer Science | Conditional Monte Carlo - Gradient Estimation and Optimization Applications

Conditional Monte Carlo

Gradient Estimation and Optimization Applications

Fu, Michael C., Jian-Qiang Hu

1997, XV, 399 p.

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  • 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.

Content Level » Research

Keywords » Internet - computer - computer science - optimization - simulation

Related subjects » Applications - Mathematics - Probability Theory and Stochastic Processes - Theoretical Computer Science

Table of contents 

Preface. Selected Notation. 1. Introduction. 2. Three Extended Examples. 3. Conditional Monte Carlo Gradient Estimation. 4. Links to Other Settings. 5. Synopsis and Preview. 6. Queueing Systems. 7. (s,S) Inventory Systems. 8. Other Applications. References. Index. Corrections to Conditional Monte Carlo: Gradient Estimation and Optimization Applications (Kluwer International Series in Engineering and Computer Science, 392) by Michael C. Fu and Jian-Qiang Hu can be found at the Internet.

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