# Modeling Uncertainty [electronic resource] : An Examination of Stochastic Theory, Methods, and Applications / edited by Moshe Dror, Pierre L’Ecuyer, Ferenc Szidarovszky.

##### Contributor(s): Dror, Moshe [editor.] | L’Ecuyer, Pierre [editor.] | Szidarovszky, Ferenc [editor.] | SpringerLink (Online service).

Material type: TextSeries: International Series in Operations Research & Management Science: 46Publisher: Boston, MA : Springer US, 2005Description: XXX, 770 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780306481024.Subject(s): Mathematics | Operations research | Decision making | Probabilities | Statistics | Mathematics | Probability Theory and Stochastic Processes | Operation Research/Decision Theory | Statistics, generalAdditional physical formats: Printed edition:: No titleDDC classification: 519.2 Online resources: Click here to access onlineProfessor Sidney J. Yakowitz -- Professor Sidney J. Yakowitz -- I -- Stability of Single Class Queueing Networks -- Sequential Optimization Under Uncertainty -- Exact Asymptotics for Large Deviation Probabilities, with Applications -- II -- Stochastic Modelling of Early HIV Immune Responses Under Treatment by Protease Inhibitors -- The Impact of Re-Using Hypodermic Needles -- Nonparametric Frequency Detection and Optimal Coding in Molecular Biology -- III -- An Efficient Stochastic Approximation Algorithm for Stochastic Saddle Point Problems -- Regression Models for Binary Time Series -- Almost Sure Convergence Properties of Nadaraya-Watson Regression Estimates -- Strategies for Sequential Prediction of Stationary Time Series -- IV -- The Birth of Limit Cycles in Nonlinear Oligopolies with Continuously Distributed Information Lags -- A Differential Game of Debt Contract Valuation -- Huge Capacity Planning and Resource Pricing for Pioneering Projects -- Affordable Upgrades of Complex Systems: A Multilevel, Performance-Based Approach -- On Successive Approximation of Optimal Control of Stochastic Dynamic Systems -- Stability of Random Iterative Mappings -- V -- ‘Unobserved’ Monte Carlo Methods for Adaptive Algorithms -- Random Search Under Additive Noise -- Recent Advances in Randomized Quasi-Monte Carlo Methods -- VI -- Singularly Perturbed Markov Chains and Applications to Large-Scale Systems under Uncertainty -- Risk-Sensitive Optimal Control in Communicating Average Markov Decision Chains -- Some Aspects of Statistical Inference in a Markovian and Mixing Framework -- VII -- Stochastic Ordering of Order Statistics II -- Vehicle Routing with Stochastic Demands: Models & Computational Methods -- Life in the Fast Lane: Yates’s Alogrithm, Fast Fourier and Walsh Transforms -- Uncertainty Bounds in Parameter Estimation with Limited Data -- A Tutorial on Hierarchical Lossless Data Compression -- VIII -- Eureka! Bellman’s Principle of Optimality is Valid! -- Reflections on Statistical Methods or Complex Stochastic Systems.

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 internionally 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.

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