附註:Includes bibliographical references and index.
Cover -- Contents -- Preface -- Contributing Authors -- 1 Professor Sidney J. Yakowitz -- Part I -- 2 Stability of Single Class Queueing Networks -- 1 Introduction -- 2 The Model -- 3 Stability: Introduction -- 4 Perturbed Liapunov Functions -- 5 Stability -- 3 Sequential Optimization Under Uncertainty -- 1 Introduction -- 2 Bandit Theory -- 3 Adaptive Control of Markov Chains -- 4 Stochastic Approximation -- 4 Exact Asymptotics for Large Deviation Probabilities, with Applications -- 1 Limit Theorems on the last negative sum and applications to nonparametric bandit theory -- 2 Large deviations in a space of trajectories -- 3 Asymptotic equivalence of the tail of the sum of independent random vectors and the tail of their maximum -- Part II -- 5 Stochastic Modelling of Early HIV Immune Responses Under Treatment by Protease Inhibitors -- 1 Introduction -- 2 A Stochastic Model of Early HIV Pathogenesis Under Treatment by a Protease Inbihitor -- 3 Mean Values of U(T)={T*(T), V0(T), V1(T)} -- 4 A State Space Model for the Early HIV Pathogenesis Under Treatment by Protease Inhibitors -- 5 An Example Using Real Data -- 6 Some Monte Carlo Studies -- 6 The impact of re-using hypodermic needles -- 1 Introduction -- 2 Geometric distribution with variable success probability -- 3 Validity of the distribution -- 4 Mean and variance of I -- 5 Intensity of epidemic -- 6 Reducing infection -- 7 The spread of the Ebola virus in 1976 -- 8 Conclusions -- 7 Nonparametric Frequency Detection and Optimal Coding in Molecular Biology -- 1 Introduction -- 2 The Spectral Envelope -- 3 Sequence Analyses -- 4 Discussion -- Part III -- 8 An Efficient Stochastic Approximation Algorithm for Stochastic Saddle Point Problems -- 1 Introduction -- 2 Stochastic saddle point problem -- 3 Discussion -- 4 Numerical Results -- 5 Conclusions -- Appendix: A: Proof of Theorems 1 and 2 -- Appendix: B: Proof of the Proposition -- 9 Regression Models for Binary Time Series -- 1 Introduction -- 2 Partial Like
摘要: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.
