Conditional Monte Carlo

Name: Conditional Monte Carlo
Brand: Springer
SKU: 9780792398738
Price: 1100.75 RON
Availability: InStock

Gradient Estimation and Optimization Applications

Autor: Michael C. Fu, Jian-Qiang Hu

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Legare: Copertă tare

Editura: Springer

Disponibilitate: În depozitul extern

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Conditional Monte Carlo: Gradient Estimation and Optimization Applications deals with various gradie...

Informații despre carte

Autor

Michael C. Fu, Jian-Qiang Hu

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

1997

Pagini

399

EAN

9780792398738

ISBN

0792398734

Enbook ID

01398512

Editura

Springer

Greutate

1680

Dimensiuni

155 x 235 x 23

Descriere completă

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.

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Conditional Monte Carlo

Gradient Estimation and Optimization Applications

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