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Fundamentals of Queueing Networks
This accessible book aims to collect in a single volume the essentials of stochastic networks. Stochastic networks have become widely used as a basic model of many physical systems in a diverse range of fields. Written by leading authors in the field, this book is meant to be used as a reference or supplementary reading by practitioners in operations research, computer systems, communications networks, production planning, and logistics.
Performance Evaluation of Complex Systems: Techniques and Tools
This book presents the tutorial lectures given by leading experts in the area at the IFIP WG 7.3 International Symposium on Computer Modeling, Measurement and Evaluation, Performance 2002, held in Rome, Italy in September 2002.The survey papers presented are devoted to theoretical and methodological advances in performance and reliability evaluation as well as new perspectives in the major application fields. Modeling and verification issues, solution methods, workload characterization, and benchmarking are addressed from the methodological point of view. Among the applications dealt with are hardware and software architectures, wired and wireless networks, grid environments, Web services, and real-time voice and video processing.This book is intended to serve as a state-of-the-art survey and reference for students, scientists, and engineers active in the area of performance and reliability evaluation.
Linear Processes in Function Spaces
The main subject of this book is the estimation and forecasting of continuous time processes. It leads to a development of the theory of linear processes in function spaces. Mathematical tools are presented, as well as autoregressive processes in Hilbert and Banach spaces and general linear processes and statistical prediction. Implementation and numerical applications are also covered. The book assumes knowledge of classical probability theory and statistics.
Stochastic Processes and Orthogonal Polynomials
The book offers an accessible reference for researchers in the probability, statistics and special functions communities. It gives a variety of interdisciplinary relations between the two main ingredients of stochastic processes and orthogonal polynomials. It covers topics like time dependent and asymptotic analysis for birth-death processes and diffusions, martingale relations for Lévy processes, stochastic integrals and Stein's approximation method. Almost all well-known orthogonal polynomials, which are brought together in the so-called Askey Scheme, come into play. This volume clearly illustrates the powerful mathematical role of orthogonal polynomials in the analysis of stochastic processes and is made accessible for all mathematicians with a basic background in probability theory and mathematical analysis. Wim Schoutens is a Postdoctoral Researcher of the Fund for Scientific Research-Flanders (Belgium). He received his PhD in Science from the Catholic University of Leuven, Belgium.
Nonparametric Statistics for Stochastic Processes
This book is devoted to the theory and applications of nonparametic functional estimation and prediction. Chapter 1 provides an overview of inequalities and limit theorems for strong mixing processes. Density and regression estimation in discrete time are studied in Chapter 2 and 3. The special rates of convergence which appear in continuous time are presented in Chapters 4 and 5. This second edition is extensively revised and it contains two new chapters. Chapter 6 discusses the surprising local time density estimator. Chapter 7 gives a detailed account of implementation of nonparametric method and practical examples in economics, finance and physics. Comarison with ARMA and ARCH methods sh...
Bayesian Learning for Neural Networks
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
Working Paper Series
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Studies in the Atmospheric Sciences
The need to understand and predict the processes that influence the Earth's atmosphere is one of the grand scientific challenges for the next century. This volume is a series of case studies and review chapters that cover many of the recent developments in statistical methodology that are useful for interpreting atmospheric data. L. Mark Berliner is Professor of Statistics at Ohio State University.
Processing Networks
The state of the art in fluid-based methods for stability analysis, giving researchers and graduate students command of the tools.
Case Studies in Environmental Statistics
This book offers a set of case studies exemplifying the broad range of statis tical science used in environmental studies and application. The case studies can be used for graduate courses in environmental statistics, as a resource for courses in statistics using genuine examples to illustrate statistical methodol ogy and theory, and for courses in environmental science. Not only are these studies valuable for teaching about an essential cross-disciplinary activity but they can also be used to spur new research along directions exposed in these examples. The studies reported here resulted from a program of research carried on by the National Institute of Statistical Sciences (NISS) during th...
