Conditional Monte Carlo: Gradient Estimation and Optimization Applications FROM THE PUBLISHER
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 notations 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.
FROM THE CRITICS
Booknews
Describes various gradient estimation techniques used in perturbation
analysis based on the use of conditional expectations. Primarily
addresses the setting of discrete-event stochastic simulations, which
is found more in variance reduction than gradient estimation. Also
considers practical applications in queuing and inventory, financial
derivatives pricing, and statistical quality control. The first part
offers a unified treatment of the theory for researchers in or
entering perturbation analysis; the second surveys applications and
describes techniques for practitioners. Assumes a good grounding in
basic probability and elementary stochastic processes; does not
require measure theory.
Annotation c. by Book News, Inc., Portland, Or.
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