Description

# Modeling uncertainty, 1st ed. 2005

An Examination of Stochastic Theory, Methods, and Applications

International Series in Operations Research & Management Science Series, Vol. 46

## Coordinators: Dror Moshe, L'Ecuyer Pierre, Szidarovszky Ferenc

Language: Anglais## Subject for *Modeling uncertainty*:

Modeling Uncertainty

Publication date: 08-2019

770 p. · 15.5x23.5 cm · Paperback

Publication date: 08-2019

770 p. · 15.5x23.5 cm · Paperback

Modeling uncertainty

Publication date: 01-2002

800 p. · Paperback

Publication date: 01-2002

800 p. · Paperback

## Description

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/li>## Biography

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Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor. Fifty internationally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume. Each of these papers was carefully reviewed and in the majority of cases the original submission was revised before being accepted for publication in the book. The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others. There are papers with a theoretical emphasis and others that focus on applications. A number of papers survey the work in a particular area and in a few papers the authors present their personal view of a topic. It is a book with a considerable number of expository articles, which are accessible to a nonexpert - a graduate student in mathematics, statistics, engineering, and economics departments, or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic. Many of the papers present the state of the art of a specific area or represent original contributions which advance the present state of knowledge. In sum, it is a book of considerable interest to a broad range of academic researchers and students of stochastic systems.

Preface. Contributing Authors. 1. Professor Sidney J. Yakowitz, D.S. Yakowitz. Part I. 2. Stability of Single Class Queueing Networks, H.J. Kushner. 3. Sequential Optimization Under Uncertainty, Tze Leung Lai. 4. Exact Asymptotics for Large Deviation Probabilities, with Applications, I. Pinelis. Part II. 5. Stochastic Modelling of Early HIV Immune Responses Under Treatment by Protease Inhibitors, Wai-Yuang Tan, Zhihuo Xiang. 6. The impact of re-using hypodermic needles, B. Barnes, J. Gani. 7. Nonparametric Frequency Detection and Optimal Coding in Molecular Biology, D.S. Stoffer. Part III. 8. An Efficient Stochastic Approximation Algorithm for Stochastic Saddle Point Problems, A. Nemirovski, R.Y. Rubinstein. 9. Regression Models for Binary Time Series, B. Kedem, K. Fokianos. 10. Almost Sure Convergence Properties of Nadaraya-Watson Regression Estimates, H. Walk. 11. Strategies for Sequential Prediction of Stationary Time Series, L. Györfi, G. Lugosi. Part IV. 12. The Birth of Limit Cycles in Nonlinear Oligopolies with Continuously Distributed Information Lag, C. Chiarella, F. Szidarovszky. 13. A Differential Game of Debt Contract Valuation, A. Haurie, F. Moresino. 14. Huge Capacity Planning and Resource Pricing for Pioneering Projects, D. Porter. 15. Affordable Upgrades of Complex Systems: A Multilevel, Performance-Based Approach, J.A. Reneke, et al. 16. On Successive Approximation of Optimal Control of Stochastic Dynamic Systems, Fei-Yue Wang, G.N. Saridis. 17. Stability of Random Iterative Mappings, L. Gerencsé,r. Part V. 18. `Unobserved' Monte Carlo Methods for Adaptive Algorithms, V. Solo. 19. Random Search Under Additive Noise, L. Devroye, A. Krzyzak. 20. Recent Advances in Randomized Quasi-Monte Carlo Methods, P. L'Ecuyer, C. Lemieux. Part VI. 21. Singularly Perturbed Markov Chains and Applications to Large-Scale Systems under Uncertainty, G. Yin, et al. 22. Risk-Sensitive Optimal Control in Communicating Average Markov Decision Chains, R. Cavazos-Cadena, E. Ferná,ndez-Gaucherand. 23. Some Aspects of Statistical Inference in a Markovian and Mixing Framework, G.G. Roussas. Part VII. 24. Stochastic Ordening of Order Statistics II, P.J. Boland, et al. 25. Vehicle Routing with Stochastic Demands: Models & Computational Methods, M. Dror. 26. Life in the Fast Lane: Yates's Algorithm, Fast Fourier and Walsh Transforms, P.J. Sanchez, et al. 27. Uncertainty Bounds in Parameter Estimation with Limited Data, J.C. Spall. 28. A Tutorial on Hierarchical Lossless Data Compression, J.C. Kieffer. Part VIII. 29. Eureka! Bellman's Principle of Optimality is valid!, M. Sniedovich. 30. Reflections on Statistical Methods for Complex Stochastic Systems, M.F. Neuts. Author Index.

**Moshe Dror**joined the Eller College of Management in 1990 and previously taught at Ben Gurion University. He earned his PhD in Management Science from the University of Maryland in 1983. His research focuses on cooperative game theory, cost allocation in inventory and supply chain management, applied combinatorial optimization in transportation, logistics and manufacturing and intelligent solution systems for operations scheduling.

**Pierre L’Ecuyer**is a Professor in the Departement d’Informatique et de Recherche Operationnelle at the Universite de Montreal, since 1990. He holds the Canada Research Chair in Stochastic Simulation and Optimization since 2004 and an Inria International Chair (at Inria-Rennes, France) for 2013–2018. He was a professor in the Departement d’Informatique at Universite Laval (Quebec) from 1983 to 1990. He is a member of the CIRRELT and GERAD research centers, in Montreal.

He has published 260 scientific articles, book chapters, and books in various areas, including random number generation, quasi-Monte Carlo methods, efficiency improvement in simulation, sensitivity analysis and optimization for discrete-event simulation models, simulation software, stochastic dynamic programming, and applications in finance, manufacturing, telecommunications, reliability, and service center management. He also developed software libraries and systems for the theoretical and empirical analysis of random number generators and quasi-Monte Carlo point sets, and for general discrete-event simulation. His Google Scholar H-index is 56 and his I10 index is 157.

**Ferenc Szidarovszky**is a senior researcher with the Ridgetop Group Inc. working on theoretical issues concerning reliability, robustness, optimal maintenance and replacement policies and prognostics in complex systems. In addition to these industrial applications he also performs research in systems theory, especially in dy

Fifty internationally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume

The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others.

Easily accessible to a nonexpert - a graduate student or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic.

The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others.

Easily accessible to a nonexpert - a graduate student or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic.

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