Conditional Monte Carlo - Fu, Michael C.

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Résumé :
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.

Biographie:
Steven I. Marcus received his Ph.D. and S.M. from the Massachusetts Institute of Technology in 1975 and 1972, respectively. He received a B.A. from Rice University in 1971. From 1975 to 1991, he was with the Department of Electrical and Computer Engineering at the University of Texas at Austin, where he was the L.B. (Preach) Meaders Professor in Engineering. He was Associate Chairman of the Department during the period 1984-89. In 1991, he joined the University of Maryland, College Park, where he was Director of the Institute for Systems Research until 1996. He is currently a Professor in the Electrical Engineering Department and the Institute for Systems Research. Steven Marcus is a Fellow of IEEE, and a member of SIAM, AMS, and the Operations Research Society of America. He is an Editor of the SIAM Journal on Control and Optimization, and Associate Editor of Mathematics of Control, Signals, and Systems, Journal on Discrete Event Dynamic Systems, and Acta Applicandae Mathematicae. He has authored or co-authored more than 100 articles, conference proceedings, and book chapters. Dr. Marcus's research interests lie in the areas of control and systems engineering, analysis and control of stochastic systems, Markov decision processes, stochastic and adaptive control, learning, fault detection, and discrete event systems, with applications in manufacturing, acoustics, and communication networks. Dr. Fu received his Ph.D. and M.S degrees in applied mathematics from Harvard University in 1989 and 1986, respectively. He received S.B. and S.M. degrees in electrical engineering and an S.B. degree in mathematics from the Massachusetts Institute of Technology in 1985. Since 1989, he has been at the University of Maryland, College Park, in the College of Business and Management. Dr. Fu is a member of IEEE and the Institute for Operations Research and the Management Sciences (INFORMS). He is the Simulation Area Editor for Operations, an Associate Editor for Management Science, and has served on the Editorial Boards of the INFORMS Journal on Computing, Production and Operations Management and IIE Transactions. He was on the program committee for the Spring 1996 INFORMS National Meeting, in charge of contributed papers. In 1995, he received the Maryland Business School's annual Allen J. Krowe Award for Teaching Excellence. He is the co-author (with Jian-Qiang Hu) of the book, Conditional Monte Carlo: Gradient Estimation and Optimization Applications (0-7923-9873-4, 1997), which received the 1998 INFORMS College on Simulation Outstanding Publication Award. Other awards include the 1999 IIE Operations Research Division Award and a 1998 IIE Transactions Best Paper Award. In 2002, he received ISR's Outstanding Systems Engineering Faculty Award. Dr. Fu's research interests lie in the areas of stochastic derivative estimation and simulation optimization of discrete-event systems, particularly with applications towards manufacturing systems, inventory control, and the pricing of financial derivatives....

Sommaire:
1 Introduction.- 1.1 Derivatives of Random Variables.- 1.2 Infinitesimal Perturbation Analysis.- 1.3 The Role of Representations.- 1.4 Basic Theoretical Tools.- 1.5 Derivatives of Measures.- 1.6 A Simple Illustrative Example.- 1.7 Two Views of Conditioning.- 1.8 A Brief Perturbation Analysis Lexicon.- 1.9 Summary.- 2 Three Extended Examples.- 2.1 Renewal Process.- 2.2 Single-Server Queue.- 2.3 (s, S) Inventory System.- 2.4 Summary.- 3 Conditional Monte Carlo Gradient Estimation.- 3.1 The GSMP Framework.- 3.2 Infinitesimal Perturbation Analysis.- 3.3 Gradient Estimation via Conditioning.- 3.4 Discontinuous Performance Measures.- 3.5 Other Stopping Times.- 3.6 Long-Run Average Performance Measures.- 3.7 Higher Order Derivative Estimators.- 4 Links to Other Settings.- 4.1 Special Cases.- 4.2 An Alternative Characterization.- 4.3 Likelihood Ratio Method.- 4.4 Rare Perturbation Analysis.- 4.5 Weak Derivatives.- 4.6 Discontinuous Perturbation Analysis.- 4.7 Augmented Infinitesimal Perturbation Analysis.- 4.8 Likelihood Ratio Method via Conditioning.- 5 Synopsis and Preview.- 5.1 Summary of Main Results.- 5.2 Efficient Implementation.- 5.3 Gradient-Based Optimization.- 5.4 Preview of Applications.- 6 Queueing Systems.- 6.1 Single Queue Notation.- 6.2 Timing Parameters.- 6.3 Discontinuous Performance Measures.- 6.4 Finite Capacity Queue.- 6.5 Priority Queue.- 6.6 Multiple Servers Second Derivative.- 6.7 Multiple Non-Identical Servers.- 6.8 The Routing Problem.- 6.9 Other Threshold-Based Parameters.- 6.10 An Optimization Example.- 6.11 Multi-Class Queueing Network.- 7 (s, S) Inventory Systems.- 7.1 Standard Periodic Review Model.- 7.2 Service Level Performance Measures.- 7.3 Hybrid Periodic Review Model.- 8 Other Applications.- 8.1 A Component Replacement Problem.- 8.2 Pricing of Financial Derivatives.- 8.3 Design of Control Charts.- References.