Matrix-analytic Methods in Stochastic Models

Many phenomena in physics, chemistry, and biology can be modelled by spatial random processes. One such process is continuum percolation, which is used when the phenomenon being modelled is made up of individual events that overlap, for example, the way individual raindrops eventually make the ground evenly wet. This is a systematic rigorous account of continuum percolation. Two models, the Boolean model and the random connection model, are treated in detail, and related continuum models are discussed. All important techniques and methods are explained and applied to obtain results on the existence of phase transitions, equality and continuity of critical densities, compressions, rarefaction, and other aspects of continuum models. This self-contained treatment, assuming only familiarity with measure theory and basic probability theory, will appeal to students and researchers in probability and stochastic geometry.

Non-commutative fields (also called skew fields or division rings) have not been studied as thoroughly as their commutative counterparts, and most accounts have hitherto been confined to division algebras - that is skew fields finite dimensional over their centre. Based on the author's LMS lecture note volume Skew Field Constructions, the present work offers a comprehensive account of skew fields. The axiomatic foundation, and a precise description of the embedding problem, is followed by an account of algebraic and topological construction methods, in particular, the author's general embedding theory is presented with full proofs, leading to the construction of skew fields. The powerful coproduct theorem of G. M. Bergman is proved here, as well as the properties of the matrix reduction functor, a useful but little-known construction providing a source of examples and counter-examples. The construction and basic properties of existentially closed skew fields are given, leading to an ex

This book examines the theory of boundary value problems for elliptic systems of partial differential equations, a theory which has many applications in mathematics and the physical sciences. The aim is to 'algebraize' the index theory by means of pseudo-differential operators and methods in the spectral theory of matrix polynomials. This latter theory provides important tools that will enable the student to work efficiently with the principal symbols of the elliptic and boundary operators on the boundary. This book is ideal for use in graduate-level courses on partial differential equations, elliptic systems, pseudo-differential operators and matrix analysis. Since many new methods and results are introduced and used throughout the book, all the theorems are proved in detail, and the methods are well illustrated through numerous examples and exercises.

Numerical Methods in Finance has emerged as a discipline at the intersection of probability theory, finance and numerical analysis. This book, based on lectures given at the Newton Institute as part of a broader programme, describes a wide variety of numerical methods used in financial analysis: computation of option prices, especially of American option prices, by finite difference and other methods; numerical solution of portfolio management strategies; statistical procedures; identification of models; Monte Carlo methods; and numerical implications of stochastic volatilities. Articles have been written in a pedagogic style and made reasonably self-contained, covering both mathematical matters and practical issues in numerical problems. Thus the book has something to offer economists, probabilists and applied mathematicians working in finance.

In early modern England, boys and girls learned to be masculine or feminine as they learned to read and write. This 1999 book explores how gender differences, instilled through specific methods of instruction in literacy, were scrutinised in the English public theatre. Close readings of plays from Shakespeare's Love's Labour's Lost to Thomas Dekker's Whore of Babylon, and of poems, didactic treatises and autobiographical writings from the same period, offer a richly textured analysis of the interaction between didactic precepts, literary models, and historical men and women. At the cross-roads between literary studies and social and cultural history, Eve Sanders' research offers insights into poems, plays, and first-person narratives (including works by women writers, such as Mary Sidney and Anne Clifford) and into the social conflicts that shaped individuals as the writers and readers of such texts.

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in

Key Concepts in Health Psychology aims to provide the reader with a comprehensive understanding of the key issues, theories, models, and methods in contemporary health psychology. It allows the reade

When is a random network (almost) connected? How much information can it carry? How can you find a particular destination within the network? And how do you approach these questions - and others - when the network is random? The analysis of communication networks requires a fascinating synthesis of random graph theory, stochastic geometry and percolation theory to provide models for both structure and information flow. This book is the first comprehensive introduction for graduate students and scientists to techniques and problems in the field of spatial random networks. The selection of material is driven by applications arising in engineering, and the treatment is both readable and mathematically rigorous. Though mainly concerned with information-flow-related questions motivated by wireless data networks, the models developed are also of interest in a broader context, ranging from engineering to social networks, biology, and physics.

Observations of ocean circulation have increased as a result of international field programmes and of remote sensing systems on artificial earth satellites. Oceanographers are increasingly turning to inverse methods for combining these observations with numerical models of ocean circulation. Professor Bennett's work explores the potential for inverse theory, emphasizing possibilities rather than expedient or rudimentary applications. In addition to interpolating the data and adding realism to the model solutions, the methods can yield estimates for unobserved flow variables, forcing fields, and model parameters. Inverse formulations can resolve ill-posed modelling problems, lead to design criteria for oceanic observing systems, and enable the testing of models as scientific hypothesis. Exercises of varying difficulty rehearse technical skills and supplement the central theoretical development. Thus this book will be invaluable for environmental scientists and engineers, advanced underg

This collection of essays, written between 1975 and 1987, covers the doctrine of analogy; the Trinity; 'theological realism'; the problems of evil and suffering; the doctrine of God, tragedy and Christian life; the doctrine of the atonement, christology, the theology of religions, ecclesiology, discipleship and the so-called theistic 'proofs'. The earlier essays reflect the author's training as a philosopher in the Anglo-American 'analytic' tradition. Later essays have a more explicitly theological focus, and they represent an attempt to come to terms with, and to proceed beyond, this tradition. More importance is attached to issues in hermeneutics and literary and social theory in the later essays. This collection therefore tends to address a wider list of topics than is usual in works of philosophical theology, and it is also distinctive in respect of the methods and approaches used in considering these topics.

Power grids, flexible manufacturing, cellular communications: interconnectedness has consequences. This remarkable book gives the tools and philosophy you need to build network models detailed enough to capture essential dynamics but simple enough to expose the structure of effective control solutions. Core chapters assume only exposure to stochastic processes and linear algebra at undergraduate level; later chapters are for advanced graduate students and researchers/practitioners. This gradual development bridges classical theory with the state-of-the-art. The workload model at the heart of traditional analysis of the single queue becomes a foundation for workload relaxations used in the treatment of complex networks. Lyapunov functions and dynamic programming equations lead to the celebrated MaxWeight policy along with many generalizations. Other topics include methods for synthesizing hedging and safety stocks, stability theory for networks, and techniques for accelerated simulation

The Annual is a ready-make toolkit of ideas, methods, techniques, and models that assist and support your work as an organizational consultant, with an emphasis in the change management area. The mate

This book teaches model-based analysis and model-based testing, with important new ways to write and analyze software specifications and designs, generate test cases, and check the results of test runs. These methods increase the automation in each of these steps, making them more timely, more thorough, and more effective. Using a familiar programming language, testers and analysts will learn to write models that describe how a program is supposed to behave. The authors work through several realistic case studies in depth and detail, using a toolkit built on the C# language and the .NET framework. Readers can also apply the methods in analyzing and testing systems in many other languages and frameworks. Intended for professional software developers including testers, and for university students, this book is suitable for courses on software engineering, testing, specification, or applications of formal methods.

This book teaches model-based analysis and model-based testing, with important new ways to write and analyze software specifications and designs, generate test cases, and check the results of test runs. These methods increase the automation in each of these steps, making them more timely, more thorough, and more effective. Using a familiar programming language, testers and analysts will learn to write models that describe how a program is supposed to behave. The authors work through several realistic case studies in depth and detail, using a toolkit built on the C# language and the .NET framework. Readers can also apply the methods in analyzing and testing systems in many other languages and frameworks. Intended for professional software developers including testers, and for university students, this book is suitable for courses on software engineering, testing, specification, or applications of formal methods.

This book studies the formation of complex societies in prehistoric China during the Neolithic and early state periods, c. 7000–1500 BC. Archaeological materials are interpreted through anthropological perspectives, using systematic analytic methods in settlement and burial patterns. Both agency and process are considered in the development of chiefdoms and in the emergence of early states in the Yellow River region. Interrelationships between factors such as mortuary practice, craft specialization, ritual activities, warfare, exchange of elite goods, climatic fluctuations, and environmental changes are emphasized. This study offers a critical evaluation of current archaeological data from Chinese sources, and argues that, although some general tendencies are noted, social changes were affected by multiple factors in no pre-determined sequence. In this most comprehensive study to date, Li Liu attempts to reconstruct developmental trajectories toward early states in Chinese civilization

Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in colour for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Highlights of the new material include: a new chapter on classification and inference, Gaussian mixture models, HMMs, hierarchical clustering, and SVMs; a new chapter on computational geometry, covering KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres; interior

IAU Symposium 240, held in conjunction with the 26th General Assembly of the International Astronomical Union, highlights the latest advances in the field of binary and multiple stars, and was the first major meeting in recent years bringing together members of both the 'wide' and 'close' binary communities. Topics discussed ranged from common proper motion pairs and other 'fragile' binaries to contact binaries and star/brown-dwarf/planet systems, exploring the numerous interests common to all binary star researchers. Highlights included talks on observing binaries using high angular and spectral resolution techniques, the impacts of new and planned large-scale surveys, recent models for binary evolution, new methods for analyzing cataclysmic pairs and disentangling spectra, surveys of extragalactic eclipsing binaries, Doppler tomography and imaging, and binaries as probes of the Galaxy's chemical and dynamical evolution. This volume will be an important reference source for binary sta

Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to kee

This book concerns the use of concepts from statistical physics in the description of financial systems. The authors illustrate the scaling concepts used in probability theory, critical phenomena, and fully developed turbulent fluids. These concepts are then applied to financial time series. The authors also present a stochastic model that displays several of the statistical properties observed in empirical data. Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling permit an understanding of the global behaviour of economic systems without first having to work out a detailed microscopic description of the system. Physicists will find the application of statistical physics concepts to economic systems interesting. Economists and workers in the financial world will find useful the presentation of empirical analysis methods and well-formulated theoretical tools that might help describe systems composed of a huge number o

Modern statistical methods use complex, sophisticated models that can lead to intractable computations. Saddlepoint approximations can be the answer. Written from the user's point of view, this book explains in clear language how such approximate probability computations are made, taking readers from the very beginnings to current applications. The core material is presented in chapters 1-6 at an elementary mathematical level. Chapters 7-9 then give a highly readable account of higher-order asymptotic inference. Later chapters address areas where saddlepoint methods have had substantial impact: multivariate testing, stochastic systems and applied probability, bootstrap implementation in the transform domain, and Bayesian computation and inference. No previous background in the area is required. Data examples from real applications demonstrate the practical value of the methods. Ideal for graduate students and researchers in statistics, biostatistics, electrical engineering, econometric

Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to kee

Ostoja-Starzewski (mechanical science and engineering, U. of Illinois, Urbana-Champaign) examines stochastic models and methods used in the mechanics of random media and illustrates these in a variety

This book details the necessary numerical methods, the theoretical background and foundations and the techniques involved in creating computer particle models, including linked-cell method, SPME-metho

This book analyzes revenue management (RM) problems with flexible products and RM in broadcasting companies. It presents models and methods that explicitly take the implications of flexibility into ac

This textbook is ideal for a course in engineering systems dynamics and controls. The work is a comprehensive treatment of the analysis of lumped parameter physical systems. Starting with a discussion of mathematical models in general, and ordinary differential equations, the book covers input/output and state space models, computer simulation and modeling methods and techniques in mechanical, electrical, thermal and fluid domains. Frequency domain methods, transfer functions and frequency response are covered in detail. The book concludes with a treatment of stability, feedback control (PID, lead-lag, root locus) and an introduction to discrete time systems. This new edition features many new and expanded sections on such topics as: solving stiff systems, operational amplifiers, electrohydraulic servovalves, using Matlab with transfer functions, using Matlab with frequency response, Matlab tutorial and an expanded Simulink tutorial. The work has 40% more end-of-chapter exercises and 3

Quantum field theory is the application of quantum mechanics to systems with infinitely many degrees of freedom. This 2007 textbook presents quantum field theoretical applications to systems out of equilibrium. It introduces the real-time approach to non-equilibrium statistical mechanics and the quantum field theory of non-equilibrium states in general. It offers two ways of learning how to study non-equilibrium states of many-body systems: the mathematical canonical way and an easy intuitive way using Feynman diagrams. The latter provides an easy introduction to the powerful functional methods of field theory, and the use of Feynman diagrams to study classical stochastic dynamics is considered in detail. The developed real-time technique is applied to study numerous phenomena in many-body systems. Complete with numerous exercises to aid self-study, this textbook is suitable for graduate students in statistical mechanics and condensed matter physics.

This book provides a pedagogical introduction to the perturbative and non-perturbative aspects of quantum chromodynamics (QCD). Introducing the basic theory and recent advances in QCD, it also reviews the historical development of the subject, covering pre-QCD ideas of strong interactions such as the quark and parton models, the notion of colours and the S-matrix approach. The author then discusses gauge theory, techniques of dimensional regularization and renormalization, deep inelastic scattering and hard processes in hadron collisions, hadron jets and e+e– annihilations. Other topics include power corrections and the technologies of the Shifman-Vainshtein-Zakharov operating product expansion. The final parts of the book are devoted to modern non-perturbative approaches to QCD and the phenomenological aspects of QCD spectral sum rules. The book will be a valuable reference for graduate students and researchers in high-energy particle and nuclear physics, both theoretical and experime

Presents the main statistical tools of econometrics, focusing specifically on modern econometric methodology. The authors unify the approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing. The choice of GMM is explained by its relevance in structural econometrics and its preeminent position in econometrics overall. Split into four parts, Part I explains general methods. Part II studies statistical models that are best suited for microeconomic data. Part III deals with dynamic models that are designed for macroeconomic and financial applications. In Part IV the authors synthesize a set of problems that are specific to statistical methods in structural econometrics, namely identification and over-identification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises. Nobel Laureate James A. Heckman offers a foreword to the work.

Presents the main statistical tools of econometrics, focusing specifically on modern econometric methodology. The authors unify the approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing. The choice of GMM is explained by its relevance in structural econometrics and its preeminent position in econometrics overall. Split into four parts, Part I explains general methods. Part II studies statistical models that are best suited for microeconomic data. Part III deals with dynamic models that are designed for macroeconomic and financial applications. In Part IV the authors synthesize a set of problems that are specific to statistical methods in structural econometrics, namely identification and over-identification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises. Nobel Laureate James A. Heckman offers a foreword to the work.

This book presents, compares, and develops various techniques for estimating market power - the ability to set price profitably above marginal cost - and strategies - the game-theoretic plans used by firms to compete with rivals. The authors start by examining static model approaches to estimating market power. They extend the analysis to dynamic models. Finally, they develop methods to estimate firms' strategies directly and examine how these strategies determine market power. A detailed technical appendix reviews the relevant information-theoretic and other econometric models that are used throughout. Questions and detailed answers for students and researchers are provided in the book for easy use.

This book presents, compares, and develops various techniques for estimating market power - the ability to set price profitably above marginal cost - and strategies - the game-theoretic plans used by firms to compete with rivals. The authors start by examining static model approaches to estimating market power. They extend the analysis to dynamic models. Finally, they develop methods to estimate firms' strategies directly and examine how these strategies determine market power. A detailed technical appendix reviews the relevant information-theoretic and other econometric models that are used throughout. Questions and detailed answers for students and researchers are provided in the book for easy use.

The interest in using Bayesian methods in ecology is increasing, however many ecologists have difficulty with conducting the required analyses. McCarthy bridges that gap, using a clear and accessible style. The text also incorporates case studies to demonstrate mark-recapture analysis, development of population models and the use of subjective judgement. The advantages of Bayesian methods, are also described here, for example, the incorporation of any relevant prior information and the ability to assess the evidence in favour of competing hypotheses. Free software is available as well as an accompanying web-site containing the data files and WinBUGS codes. Bayesian Methods for Ecology will appeal to academic researchers, upper undergraduate and graduate students of Ecology.

With the current push toward educational reform, there is great potential for innovation and change, particularly in large scale testing. One area where change is possible is in cognitive diagnostic assessment. Researchers in educational measurement and cognitive psychology are finally in a position to design tests targeted specifically for providing valuable information about students' cognitive strengths and weaknesses. This self-contained volume organizes what is known about cognitive diagnostic assessment in education, including its conceptual and philosophical basis, methods, and applications. The complete list of topics includes educational demand, philosophical rationale, construct validity, cognitive methods, test construction, statistical models, and unresolved issues (e.g., how to best translate diagnostic information into teaching practices). Leighton and Gierl present a comprehensive and up-to-date examination of cognitive diagnostic assessment in education.

This book offers a practical understanding of issues involved in improving data quality through editing, imputation, and record linkage. The first part of the book deals with methods and models, focus

With the current push toward educational reform, there is great potential for innovation and change, particularly in large scale testing. One area where change is possible is in cognitive diagnostic assessment. Researchers in educational measurement and cognitive psychology are finally in a position to design tests targeted specifically for providing valuable information about students' cognitive strengths and weaknesses. This self-contained volume organizes what is known about cognitive diagnostic assessment in education, including its conceptual and philosophical basis, methods, and applications. The complete list of topics includes educational demand, philosophical rationale, construct validity, cognitive methods, test construction, statistical models, and unresolved issues (e.g., how to best translate diagnostic information into teaching practices). Leighton and Gierl present a comprehensive and up-to-date examination of cognitive diagnostic assessment in education.

Written by a group of international experts in their field, this book is a review of Lagrangian observation, analysis and assimilation methods in physical and biological oceanography. This multidisciplinary text presents new results on nonlinear analysis of Lagrangian dynamics, the prediction of particle trajectories, and Lagrangian stochastic models. It includes historical information, up-to-date developments, and speculation on future developments in Lagrangian-based observations, analysis, and modeling of physical and biological systems. Containing contributions from experimentalists, theoreticians, and modelers in the fields of physical oceanography, marine biology, mathematics, and meteorology, this book will be of great interest to researchers and graduate students looking for both practical applications and information on the theory of transport and dispersion in physical systems, biological modeling, and data assimilation.

The book constitutes a particularly complete and original collection of ideas, models, numerical methods and experimental tools which will prove invaluable in the study of microscale and nanoscale hea

Several of the ten papers in this collection explore modeling methods for active domains, knowledge management, deontic business rules, and organizational memory. Other topics include matching models

Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title,