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Dealing with Data is an introductory course to problems and techniques dealing with data analysis, with emphasis on the physical and engineering sciences. The book starts with the basics of data analysis through non-statistical and non-mathematical assessments of error and uncertainty conditions. Experimental and maximum errors and the use of simple graphical methods are briefly described. Applying quick methods on data analysis such as frequency distributions, determination of standard errors, and applications of significance tests are explained. Special attention is given to the statistical quick methods where the range is preferred to traditional methods of calculation. The author notes that the quick methods have more practical applications in physics and engineering. The use of the quick methods of calculation is more precise in error estimation, confidence limits, and tests for outliers that the error is very negligible when applied to actual conditions. Dealing with errors of computation arising from rounding of values, and those arising from the use of slide rules and of the logarithm tables, is explained. The use of numerical methods (integration, differentiation, and interpolation) and graphical methods (like curve fitting) is briefly explained, with the author's emphasis on choosing the simpler methods. Sixth formers, engineering undergraduates, statisticians, and students of mathematics will find the information in this book useful.

The book that follows is an experiment in the teaching of population theory and analysis. A sequence of problems where each is a self-contained puzzle, and the successful solution of each which puts the student in a position to tackle the next, is a means of securing the active participation of the learner and so the mastery of a technical subject. How far our questions are the exciting puzzles at which we aimed, and how far the sequence constitutes a rounded course in demography, must be left to the user to judge. One test of a good problem is whether a solution, that may take hours of cogitation, is immediately recognizable once it comes to mind. While algebraic manipulation is required throughout, we have tried to emphasize problems in which there is some substantive point-a conclusion regarding population that can be put into words. Our title, Demography Through Problems, reflects our intention of leading the reader who will actively commit him-or herself through a sequence that will not only teach definitions-in itself a trivial matter-but sharpen intuition on the way that populations behave.

Over the last fifty years advanced mathematical tools have become an integral part in the development of modern economic theory. Economists continue to invoke sophisticated mathematical techniques and ideas in order to understand complex economic and social problems. In the last ten years the theory of Riesz spaces (vector lattices) has been successfully applied to economic theory. By now it is understood relatively well that the lattice structure of Riesz spaces can be employed to capture and interpret several economic notions. On April 16-20, 1990, a small conference on Riesz Spaces, Positive Opera­ tors, and their Applications to Economics took place at the California Institute of Technology. The purpose of the conference was to bring mathematicians special­ ized in Riesz Spaces and economists specialized in General Equilibrium together to exchange ideas and advance the interdisciplinary cooperation between math­ ematicians and economists. This volume is a collection of papers that represent the talks and discussions of the participants at the week-long conference. We take this opportunity to thank all the participants of the conference, especially those whose articles are contained in this volume. We also greatly ap­ preciate the financial support provided by the California Institute of Technology. In particular, we express our sincerest thanks to David Grether, John Ledyard, and David Wales for their support. Finally, we would like to thank Susan Davis, Victoria Mason, and Marge D'Elia who handled the delicate logistics for the smooth running of the confer­ ence.

The 1992 Seminar on Stochastic Processes was held at the Univer­ sity of Washington from March 26 to March 28, 1992. This was the twelfth in a series of annual meetings which provide researchers with the opportunity to discuss current work on stochastic processes in an informal and enjoyable atmosphere. Previous seminars were held at Northwestern University, Princeton University, University of Florida, University of Virginia, University of California, San Diego, University of British Columbia and University of California, Los An­ geles. Following the successful format of previous years, there were five invited lectures, delivered by R. Adler, R. Banuelos, J. Pitman, S. J. Taylor and R. Williams, with the remainder of the time being devoted to informal communications and workshops on current work and problems. The enthusiasm and interest of the participants cre­ ated a lively and stimulating atmosphere for the seminar. A sample of the research discussed there is contained in this volume. The 1992 Seminar was made possible through the support of the National Science Foundation, the National Security Agency, the Institute of Mathematical Statistics and the University of Washing­ ton. We extend our thanks to them and to the publisher Birkhauser Boston for their support and encouragement. Richard F. Bass Krzysztof Burdzy Seattle, 1992 SUPERPROCESS LOCAL AND INTERSECTION LOCAL TIMES AND THEIR CORRESPONDING PARTICLE PICTURES Robert J.

Non-Additive Measure and Integral is the first systematic approach to the subject. Much of the additive theory (convergence theorems, Lebesgue spaces, representation theorems) is generalized, at least for submodular measures which are characterized by having a subadditive integral. The theory is of interest for applications to economic decision theory (decisions under risk and uncertainty), to statistics (including belief functions, fuzzy measures) to cooperative game theory, artificial intelligence, insurance, etc. Non-Additive Measure and Integral collects the results of scattered and often isolated approaches to non-additive measures and their integrals which originate in pure mathematics, potential theory, statistics, game theory, economic decision theory and other fields of application. It unifies, simplifies and generalizes known results and supplements the theory with new results, thus providing a sound basis for applications and further research in this growing field of increasing interest. It also contains fundamental results of sigma-additive and finitely additive measure and integration theory and sheds new light on additive theory. Non-Additive Measure and Integral employs distribution functions and quantile functions as basis tools, thus remaining close to the familiar language of probability theory. In addition to serving as an important reference, the book can be used as a mathematics textbook for graduate courses or seminars, containing many exercises to support or supplement the text.

The numerical solution of stochastic differential equations is becoming an in­ dispensible worktool in a multitude of disciplines, bridging a long-standing gap between the well advanced theory of stochastic differential equations and its application to specific examples. This has been made possible by the much greater accessibility to high-powered computers at low-cost combined with the availability of new, effective higher order numerical schemes for stochastic dif­ ferential equations. Many hitherto intractable problems can now be tackled successfully and more realistic modelling with stochastic differential equations undertaken. The aim of this book is to provide a computationally oriented introduction to the numerical solution of stochastic differential equations, using computer experiments to develop in the readers an ability to undertake numerical studies of stochastic differential equations that arise in their own disciplines and an understanding, intuitive at least, of the necessary theoretical background. It is related to, but can also be used independently of the monograph P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations, Applications of Mathematics Series Vol. 23, Springer-Verlag, Hei­ delberg, 1992, which is more theoretical, presenting a systematic treatment of time-discretized numerical schemes for stochastic differential equations along with background material on probability and stochastic calculus. To facilitate the parallel use of both books, the presentation of material in this book follows that in the monograph closely.

As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors'general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control.

This volume presents the published proceedings of the lOth International Workshop on Statistical Modelling, to be held in Innsbruck, Austria from 10 to 14 July, 1995. This workshop marks an important anniversary. The inaugural workshop in this series also took place in Innsbruck in 1986, and brought together a small but enthusiastic group of thirty European statisticians interested in statistical modelling. The workshop arose out of two G LIM conferences in the U. K. in London (1982) and Lancaster (1985), and from a num­ ber of short courses organised by Murray Aitkin and held at Lancaster in the early 1980s, which attracted many European statisticians interested in Generalised Linear Modelling. The inaugural workshop in Innsbruck con­ centrated on GLMs and was characterised by a number of features - a friendly and supportive academic atmosphere, tutorial sessions and invited speakers presenting new developments in statistical modelling, and a very well organised social programme. The academic programme allowed plenty of time for presentation and for discussion, and made available copies of all papers beforehand. Over the intervening years, the workshop has grown substantially, and now regularly attracts over 150 participants. The scope of the workshop is now much broader, reflecting the growth in the subject of statistical modelling over ten years. The elements ofthe first workshop, however, are still present, and participants always find the meetings relevant and stimulating.

Stochastic programming - the science that provides us with tools to design and control stochastic systems with the aid of mathematical programming techniques - lies at the intersection of statistics and mathematical programming. The book Stochastic Programming is a comprehensive introduction to the field and its basic mathematical tools. While the mathematics is of a high level, the developed models offer powerful applications, as revealed by the large number of examples presented. The material ranges form basic linear programming to algorithmic solutions of sophisticated systems problems and applications in water resources and power systems, shipbuilding, inventory control, etc. Audience: Students and researchers who need to solve practical and theoretical problems in operations research, mathematics, statistics, engineering, economics, insurance, finance, biology and environmental protection.

The aim of this graduate textbook is to provide a comprehensive advanced course in the theory of statistics covering those topics in estimation, testing, and large sample theory which a graduate student might typically need to learn as preparation for work on a Ph.D. An important strength of this book is that it provides a mathematically rigorous and even-handed account of both Classical and Bayesian inference in order to give readers a broad perspective. For example, the'uniformly most powerful'approach to testing is contrasted with available decision-theoretic approaches.

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.

Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, do not have an overview whereby they can see that the three areas - linear normal, categorical, and survival models - have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to scientists with a basic grounding in modern statistics. With the many exercises included at the ends of chapters, it will be an excellent text for teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler normal linear models, regression and ANOVA. The author is professor in the biostatistics department at Limburgs University, Diepenbeek, in the social science department at the University of Liège, and in medical statistics at DeMontfort University, Leicester. He is the author of nine other books.

Both in insurance and in finance applications, questions involving extremal events (such as large insurance claims, large fluctuations in financial data, stock market shocks, risk management,...) play an increasingly important role. This book sets out to bridge the gap between the existing theory and practical applications both from a probabilistic as well as from a statistical point of view. Whatever new theory is presented is always motivated by relevant real-life examples. The numerous illustrations and examples, and the extensive bibliography make this book an ideal reference text for students, teachers and users in the industry of extremal event methodology.

From the acclaimed Los Angeles Times science writer, a wise, witty, and elegant study of how math provides practical solutions to everyday problems. Are the secrets of the universe written in words—or is it all about the digits? K.C. Cole follows up her paean to the power of physics, Sympathetic Vibrations, with this engaging and accessible guide to the might and majesty of mathematics. The Universe and the Teacup uses relatable examples, humorous prose, and whimsical line drawings to demonstrate math's ability to “translate the complexity of the world into manageable patterns.” Cole shows how mathematical concepts illuminate everything from human risk-taking behavior to astronomical investigation, game theory to logic problems—not to mention the very structure of the universe itself. Brimming with trivia stressing the importance of math throughout history, this is a book both math nerds and the “innumerate” everyday person can enjoy in equal measure. “Cole writes clearly, simply and vividly,” noted The New York Times. “She so obviously likes mathematics, the reader can't help liking it too.” Filled with “a thousand fascinating facts and shrewd observations (Martin Gardner, Los Angeles Times), this book demonstrates how the truth and beauty of everything, from relativity to rainbows, is all in the numbers.

This is a history of the use of Bayes theoremfrom its discovery by Thomas Bayes to the rise of the statistical competitors in the first part of the twentieth century. The book focuses particularly on the development of one of the fundamental aspects of Bayesian statistics, and in this new edition readers will find new sections on contributors to the theory. In addition, this edition includes amplified discussion of relevant work.

Prediction of a random field based on observations of the random field at some set of locations arises in mining, hydrology, atmospheric sciences, and geography. Kriging, a prediction scheme defined as any prediction scheme that minimizes mean squared prediction error among some class of predictors under a particular model for the field, is commonly used in all these areas of prediction. This book summarizes past work and describes new approaches to thinking about kriging.

As is well known, Pontryagin's maximum principle and Bellman's dynamic programming are the two principal and most commonly used approaches in solving stochastic optimal control problems. • An interesting phenomenon one can observe from the literature is that these two approaches have been developed separately and independently. Since both methods are used to investigate the same problems, a natural question one will ask is the fol­ lowing: (Q) What is the relationship betwccn the maximum principlc and dy­ namic programming in stochastic optimal controls? There did exist some researches (prior to the 1980s) on the relationship between these two. Nevertheless, the results usually werestated in heuristic terms and proved under rather restrictive assumptions, which were not satisfied in most cases. In the statement of a Pontryagin-type maximum principle there is an adjoint equation, which is an ordinary differential equation (ODE) in the (finite-dimensional) deterministic case and a stochastic differential equation (SDE) in the stochastic case. The system consisting of the adjoint equa­ tion, the original state equation, and the maximum condition is referred to as an (extended) Hamiltonian system. On the other hand, in Bellman's dynamic programming, there is a partial differential equation (PDE), of first order in the (finite-dimensional) deterministic case and of second or­ der in the stochastic case. This is known as a Hamilton-Jacobi-Bellman (HJB) equation.

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place.Most analyses were done with the MIXED procedure of the SAS software package, but the data analyses are presented in a software-independent fashion.

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.The necessary mathematical tools are presented in Chapters 1 and 2. Chapters 3 to 6 deal with autoregressive processes in Hilbert and Banach spaces. Chapter 7 is devoted to general linear processes and Chapter 8 with statistical prediction. Implementation and numerical applications appear in Chapter 9. The book assumes a knowledge of classical probability theory and statistics. Denis Bosq is Professor of Statistics at the University of Paris 6 (Pierre et Marie Curie). He is Chief-Editor of Statistical Inference for Stochastic Processes and of Annales de l'ISUP, and Associate Editor of the Journal of Nonparametric Statistics. He is an elected member of the International Statistical Institute, and he has published about 100 papers or works on nonparametric statistics and five books including Nonparametric Statistics for Stochastic Processes: Estimation and Prediction, Second Edition (Springer, 1998).

Counting is hard.'Counting'is short for'Enumerative Combinatorics,'which certainly doesn't sound easy. This book provides an introduction to discrete mathematics that addresses questions that begin, How many ways are there to.... At the end of the book the reader should be able to answer such nontrivial counting questions as, How many ways are there to stack n poker chips, each of which can be red, white, blue, or green, such that each red chip is adjacent to at least 1 green chip? There are no prerequisites for this course beyond mathematical maturity. The book can be used for a semester course at the sophomore level as introduction to discrete mathematics for mathematics, computer science, and statistics students. The first five chapters can also serve as a basis for a graduate course for in-service teachers.

Presents previously unpublished material on the fundumental pronciples and properties of Orlicz sequence and function spaces. Examines the sample path behavior of stochastic processes. Provides practical applications in statistics and probability.

It is increasingly clear that good quantitative work in the environmental sciences must be genuinely interdisciplinary. This volume, the proceedings of the first combined TIES/SPRUCE conference held at the University of Sheffield in September 2000, well demonstrates the truth of this assertion, highlighting the successful use of both statistics and mathematics in important practical problems.It brings together distinguished scientists and engineers to present the most up-to-date and practical methods for quantitative measurement and prediction and is organised around four themes:- spatial and temporal models and methods;- environmental sampling and standards;- atmosphere and ocean;- risk and uncertainty.Quantitative Methods for Current Environmental Issues is an invaluable resource for statisticians, applied mathematicians and researchers working on environmental problems, and for those in government agencies and research institutes involved in the analysis of environmental issues.

Designed to help motivate the learning of advanced calculus by demonstrating its relevance in the field of statistics, this successful text features detailed coverage of optimization techniques and their applications in statistics while introducing the reader to approximation theory. The Second Edition provides substantial new coverage of the material, including three new chapters and a large appendix that contains solutions to almost all of the exercises in the book. Applications of some of these methods in statistics are discusses.

This text is a basic introduction to those areas of discrete mathematics used by stu­ dents of mathematics and computer science. Introductory courses on this material are now standard at many colleges and universities. Usually these courses are of one semester's duration, and usually they are offered at the sophomore level. Very often this will be the first course where the students see several real proofs. The preparation of the students is very mixed, and one cannot assume a strong back­ ground. In particular, the instructor should not assume that the students have seen a linear algebra course, or any introduction to number systems that goes beyond college algebra. In view of this, I have tried to avoid too much sophistication, while still re­ taining rigor. I hope I have included enough problems so that the student can reinforce the concepts. Most of the problems are quite easy, with just a few dif­ ficult exercises scattered through the text. If the class is weak, a small number of sections will be too hard, while the instructor who has a strong class will need to include some supplementary material. I think this is preferable to a book at a higher mathematical level, which will scare away weaker students.

A look at baseball data from a statistical modeling perspective! There is a fascination among baseball fans and the media to collect data on every imaginable event during a baseball game and this book addresses a number of questions that are of interest to many baseball fans. These include how to rate players, predict the outcome of a game or the attainment of an achievement, making sense of situational data, and deciding the most valuable players in the World Series. Aimed at a general audience, the text does not assume any prior background in probability or statistics, although a knowledge of high school abgebra will be helpful.

In linear regression the ordinary least squares estimator plays a central role and sometimes one may get the impression that it is the only reasonable and applicable estimator available. Nonetheless, there exists a variety of alterna­ tives, proving useful in specific situations. Purpose and Scope. This book aims at presenting a comprehensive survey of different point estimation methods in linear regression, along with the the­ oretical background on a advanced courses level. Besides its possible use as a companion for specific courses, it should be helpful for purposes of further reading, giving detailed explanations on many topics in this field. Numerical examples and graphics will aid to deepen the insight into the specifics of the presented methods. For the purpose of self-containment, the basic theory of linear regression models and least squares is presented. The fundamentals of decision theory and matrix algebra are also included. Some prior basic knowledge, however, appears to be necessary for easy reading and understanding.

Big Queues aims to give a simple and elegant account of how large deviations theory can be applied to queueing problems. Large deviations theory is a collection of powerful results and general techniques for studying rare events, and has been applied to queueing problems in a variety of ways. The strengths of large deviations theory are these: it is powerful enough that one can answer many questions which are hard to answer otherwise, and it is general enough that one can draw broad conclusions without relying on special case calculations.

Bond markets differ in one fundamental aspect from standard stock markets. While the latter are built up to a finite number of trade assets, the underlying basis of a bond market is the entire term structure of interest rates: an infinite-dimensional variable which is not directly observable. On the empirical side, this necessitates curve-fitting methods for the daily estimation of the term structure. Pricing models, on the other hand, are usually built upon stochastic factors representing the term structure in a finite-dimensional state space. Written for readers with knowledge in mathematical finance (in particular interest rate theory) and elementary stochastic analysis, this research monograph has threefold aims: to bring together estimation methods and factor models for interest rates, to provide appropriate consistency conditions and to explore some important examples.

Unparalled coverage of parametric and nonparametric statistical procedures. Details more than 75 statistical procedures. Contains information on how to select and use the appropriate statistical analysis for evaluating data from an empirical study.

The book presents important tools and techniques for treating problems in m- ern multivariate statistics in a systematic way. The ambition is to indicate new directions as well as to present the classical part of multivariate statistical analysis in this framework. The book has been written for graduate students and statis- cians who are not afraid of matrix formalism. The goal is to provide them with a powerful toolkit for their research and to give necessary background and deeper knowledge for further studies in di?erent areas of multivariate statistics. It can also be useful for researchers in applied mathematics and for people working on data analysis and data mining who can?nd useful methods and ideas for solving their problems. Ithasbeendesignedasatextbookforatwosemestergraduatecourseonmultiva- ate statistics. Such a course has been held at the Swedish Agricultural University in 2001/02. On the other hand, it can be used as material for series of shorter courses. In fact, Chapters 1 and 2 have been used for a graduate course ”Matrices in Statistics” at University of Tartu for the last few years, and Chapters 2 and 3 formed the material for the graduate course ”Multivariate Asymptotic Statistics” in spring 2002. An advanced course ”Multivariate Linear Models” may be based on Chapter 4. A lot of literature is available on multivariate statistical analysis written for di?- ent purposes and for people with di?erent interests, background and knowledge.

This book focuses on various aspects of dynamic game theory, presenting state-of-the-art research and serving as a guide to the vitality and growth of the field and its applications. The selected chapters, written by experts in their respective disciplines, are an outgrowth of presentations originally given at the 9th International Symposium of Dynamic Games and Applications. Featured throughout are useful tools for researchers and practitioners who use game theory for modeling in many disciplines. Major topics covered include: • repeated and stochastic games • differential dynamic games • optimal stopping games • applications of dynamic games to economics, finance, and queuing theory • numerical methods and algorithms for solving dynamic games • Parrondo's games and related topics A valuable reference for practitioners and researchers in dynamic game theory, the book and its diverse applications will also benefit researchers and graduate students in applied mathematics, economics, engineering, systems and control, and environmental science.

Traditional methods of biometric analysis are unable to overcome the limitations of existing approaches, mainly due to the lack of standards for input data, privacy concerns involving use and storage of actual biometric data, and unacceptable accuracy. Exploring solutions to inverse problems in biometrics transcends such limits and allows rich analysis of biometric information and systems for improved performance and testing. Although some particular inverse problems appear in the literature, until now there has been no comprehensive reference for these problems.Biometric Inverse Problems provides the first comprehensive treatment of biometric data synthesis and modeling. This groundbreaking reference comprises eight self-contained chapters that cover the principles of biometric inverse problems; basics of data structure design; new automatic synthetic signature, fingerprint, and iris design; synthetic faces and DNA; and new tools for biometrics based on Voronoi diagrams. Based on the authors'vast experience in the field, the book authoritatively examines new approaches and methodologies in both direct and inverse biometrics, providing invaluable analytical and benchmarking tools. The authors include case studies, examples, and implementation codes for practical illustration of the methods.Loaded with approximately 200 figures, 60 problems, 50 MATLAB® code fragments, and 200 examples, Biometric Inverse Problems sets the standard for innovation and authority in biometric data synthesis, modeling, and analysis.

Exploratory data analysis (EDA) was conceived at a time when computers were not widely used, and thus computational ability was rather limited. As computational sophistication has increased, EDA has become an even more powerful process for visualizing and summarizing data before making model assumptions to generate hypotheses, encompassing larger a

Con el presente libro se pretende ofrecer un texto básico para un curso de Inferencia Estadística en Matemáticas, Economía, informática, Ingenierías, Medicina, Biología, Psicología, Sociología y en definitiva para los estudios que necesitan modelizar la incertidumbre mediante la probabilidad para extraer conclusiones de esta modernización.Contiene los conceptos y métodos de la Estadística moderna: un equilibrio entre el análisis de datos - con especial atención a las representaciones gráficas- y los modelos matemáticos, ambas cosas realizadas con el creciente uso delordenador.INDICE: Introducción a la estadística matemática y estadísticos muestrales.Reducción de datos. Estimación puntual paramétrica. Estimación por regiones de confianza. Contrastes de hipótesis. Teoría de la decisión. Análisis de la varianza y regresión lineal. Estadística no paramétrica.

In this book invariant probabilities for a large class of discrete-time homogeneous Markov processes known as Feller processes are discussed. These Feller processes appear in the study of iterated function systems with probabilities, convolution operators, certain time series, etc. Rather than dealing with the processes, the transition probabilities and the operators associated with these processes are studied.Main features:- an ergodic decomposition which is a'reference system'for dealing with ergodic measures-'formulas'for the supports of invariant probability measures, some of which can be used to obtain algorithms for the graphical display of these supports- helps to gain a better understanding of the structure of Markov-Feller operators, and, implicitly, of the discrete-time homogeneous Feller processes- special efforts to attract newcomers to the theory of Markov processes in general, and to the topics covered in particular- most of the results are new and deal with topics of intense research interest.

In the Preface to the first edition, originally published in 1980, we mentioned that this book was based on the author's lectures in the Department of Mechanics and Mathematics of the Lomonosov University in Moscow, which were issued, in part, in mimeographed form under the title'Probabil­ ity, Statistics, and Stochastic Processors, I, II'and published by that Univer­ sity. Our original intention in writing the first edition of this book was to divide the contents into three parts: probability, mathematical statistics, and theory of stochastic processes, which corresponds to an outline of a three­ semester course of lectures for university students of mathematics. However, in the course of preparing the book, it turned out to be impossible to realize this intention completely, since a full exposition would have required too much space. In this connection, we stated in the Preface to the first edition that only probability theory and the theory of random processes with discrete time were really adequately presented. Essentially all of the first edition is reproduced in this second edition. Changes and corrections are, as a rule, editorial, taking into account com­ ments made by both Russian and foreign readers of the Russian original and ofthe English and Germantranslations [Sll]. The author is grateful to all of these readers for their attention, advice, and helpful criticisms. In this second English edition, new material also has been added, as follows: in Chapter 111, §5, §§7-12; in Chapter IV, §5; in Chapter VII, §§8-10.

This volume is the first of two volumes containing the revised and completed notes lectures given at the school'Quantum Independent Increment Processes: Structure and Applications to Physics'. This school was held at the Alfried-Krupp-Wissenschaftskolleg in Greifswald during the period March 9 – 22, 2003, and supported by the Volkswagen Foundation. The school gave an introduction to current research on quantum independent increment processes aimed at graduate students and non-specialists working in classical and quantum probability, operator algebras, and mathematical physics. The present first volume contains the following lectures:'Lévy Processes in Euclidean Spaces and Groups'by David Applebaum,'Locally Compact Quantum Groups'by Johan Kustermans,'Quantum Stochastic Analysis'by J. Martin Lindsay, and'Dilations, Cocycles and Product Systems'by B.V. Rajarama Bhat.

This is the second of two volumes containing the revised and completed notes of lectures given at the school'Quantum Independent Increment Processes: Structure and Applications to Physics'. This school was held at the Alfried-Krupp-Wissenschaftskolleg in Greifswald in March, 2003, and supported by the Volkswagen Foundation. The school gave an introduction to current research on quantum independent increment processes aimed at graduate students and non-specialists working in classical and quantum probability, operator algebras, and mathematical physics. The present second volume contains the following lectures:'Random Walks on Finite Quantum Groups'by Uwe Franz and Rolf Gohm,'Quantum Markov Processes and Applications in Physics'by Burkhard Kümmerer, Classical and Free Infinite Divisibility and Lévy Processes'by Ole E. Barndorff-Nielsen, Steen Thorbjornsen, and'Lévy Processes on Quantum Groups and Dual Groups'by Uwe Franz.

Applied probability is a broad research area that is of interest to scientists in diverse disciplines in science and technology, including: anthropology, biology, communication theory, economics, epidemiology, finance, geography, linguistics, medicine, meteorology, operations research, psychology, quality control, sociology, and statistics. Recent Advances in Applied Probability is a collection of survey articles that bring together the work of leading researchers in applied probability to present current research advances in this important area. This volume will be of interest to graduate students and researchers whose research is closely connected to probability modelling and their applications. It is suitable for one semester graduate level research seminar in applied probability.

Peter Kall and János Mayer are distinguished scholars and professors of Operations Research and their research interest is particularly devoted to the area of stochastic optimization. Stochastic Linear Programming: Models, Theory, and Computation is a definitive presentation and discussion of the theoretical properties of the models, the conceptual algorithmic approaches, and the computational issues relating to the implementation of these methods to solve problems that are stochastic in nature. The application area of stochastic programming includes portfolio analysis, financial optimization, energy problems, random yields in manufacturing, risk analysis, etc. In this book, models in financial optimization and risk analysis are discussed as examples, including solution methods and their implementation. Stochastic programming is a fast developing area of optimization and mathematical programming. Numerous papers and conference volumes, and several monographs have been published in the area; however, the Kall and Mayer book will be particularly useful in presenting solution methods including their solid theoretical basis and their computational issues, based in many cases on implementations by the authors. The book is also suitable for advanced courses in stochastic optimization.

Stochastic geometry is a relatively new branch of mathematics. Although its predecessors such as geometric probability date back to the 18th century, the formal concept of a random set was developed in the beginning of the 1970s. Theory of Random Sets presents a state of the art treatment of the modern theory, but it does not neglect to recall and build on the foundations laid by Matheron and others, including the vast advances in stochastic geometry, probability theory, set-valued analysis, and statistical inference of the 1990s. The book is entirely self-contained, systematic and exhaustive, with the full proofs that are necessary to gain insight. It shows the various interdisciplinary relationships of random set theory within other parts of mathematics, and at the same time, fixes terminology and notation that are often varying in the current literature to establish it as a natural part of modern probability theory, and to provide a platform for future development.

Stochastic differential equations (SDEs) are a powerful tool in science, mathematics, economics and finance. This book will help the reader to master the basic theory and learn some applications of SDEs. In particular, the reader will be provided with the backward SDE technique for use in research when considering financial problems in the market, and with the reflecting SDE technique to enable study of optimal stochastic population control problems. These two techniques are powerful and efficient, and can also be applied to research in many other problems in nature, science and elsewhere.

The paradigms of dynamic games play an important role in the development of multi-agent models in engineering, economics, and management science. The applicability of their concepts stems from the ability to encompass situations with uncertainty, incomplete information, fluctuating coalition structure, and coupled constraints imposed on the strategies of all the players. This book—an outgrowth of the 10th International Symposium on Dynamic Games—presents current developments of the theory of dynamic games and its applications to various domains, in particular energy-environment economics and management sciences. The volume uses dynamic game models of various sorts to approach and solve several problems pertaining to pursuit-evasion, marketing, finance, climate and environmental economics, resource exploitation, as well as auditing and tax evasions. In addition, it includes some chapters on cooperative games, which are increasingly drawing dynamic approaches to their classical solutions. The book is thematically organized into six parts: • zero-sum game theory • pursuit-evasion games • games of coalitions • new interpretations of the interdependence between different members of a social group • original applications to energy-environment economics • management science applications This work will serve as a state-of-the art account of recent advances in dynamic game theory and its applications for researchers, practitioners, and graduate students in applied mathematics, engineering, economics, as well as environmental and management sciences.

This book is devoted to the study and optimization of spatiotem- ral stochastic processes. These are processes that simultaneously - velop in space and time under random influences. Such processes - cur almost everywhere when the global behavior of complex systems is studied, e.g., in physical and technical systems, population dynamics, neural networks, computer and telecommunication networks, complex production networks and flexible manufacturing systems, logistic n- works and transportation systems, environmental engineering, climate modeling and prediction, earth surface models, and so on. In the study of spatiotemporal stochastic processes the classical c- cepts of random fields (which are models for spatially distributed r- dom phenomena) and of stochastic processes (hich are usually thought to describe the evolution over time of systems under random influences) converge. Over the last twenty years,many research monographs were written with emphasis on this unifying point of view, as were a huge number of articles and papers on this subject.

Lagrangian expansions can be used to obtain numerous useful probability models, which have been applied to real life situations including, but not limited to: branching processes, queuing processes, stochastic processes, environmental toxicology, diffusion of information, ecology, strikes in industries, sales of new products, and production targets for optimum profits. This book presents a comprehensive, systematic treatment of the class of Lagrangian probability distributions, along with some of its families, their properties, and important applications. Graduate students and researchers with a good knowledge of standard statistical techniques and an interest in Lagrangian probability distributions will find this work valuable. It may be used as a reference text or in courses and seminars on Distribution Theory and Lagrangian Distributions. Applied scientists and researchers in environmental statistics, reliability, sales management, epidemiology, operations research, optimization in manufacturing and marketing, and infectious disease control will benefit immensely from the various applications in the book.

An effective technique for data analysis in the social sciences The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and findings and, at the same time, provide original results. The book analyzes LCMs from the perspective of structural equation models (SEMs) with latent variables. While the authors discuss simple regression-based procedures that are useful in the early stages of LCMs, most of the presentation uses SEMs as a driving tool. This cutting-edge work includes some of the authors'recent work on the autoregressive latent trajectory model, suggests new models for method factors in multiple indicators, discusses repeated latent variable models, and establishes the identification of a variety of LCMs. This text has been thoroughly class-tested and makes extensive use of pedagogical tools to aid readers in mastering and applying LCMs quickly and easily to their own data sets. Key features include: Chapter introductions and summaries that provide a quick overview of highlights Empirical examples provided throughout that allow readers to test their newly found knowledge and discover practical applications Conclusions at the end of each chapter that stress the essential points that readers need to understand for advancement to more sophisticated topics Extensive footnoting that points the way to the primary literature for more information on particular topics With its emphasis on modeling and the use of numerous examples, this is an excellent book for graduate courses in latent trajectory models as well as a supplemental text for courses in structural modeling. This book is an excellent aid and reference for researchers in quantitative social and behavioral sciences who need to analyze longitudinal data.