Matrix-analytic Methods in Stochastic Models

Word stress has long presented challenges to phonologists, as they have sought to uncover patterns in its distribution, and devise models to account for its behaviour and formal representation both within single languages and cross-linguistically. In this collection, a team of world-renowned researchers present a variety of viewpoints on the methods and problems involved. Offering fresh perspectives on the topic and its study, this book is specifically concerned with basing theoretical work on broad typological surveys and focuses on the collection, selection and use of data in the analysis of word stress and word rhythm, including their phonetic manifestations. An extensive introduction presents a state-of-the-art review of stress research. The contributors also present StressTyp2, a project in an advanced stage of development, which intends to make publicly available information on word stress in a broad sample of languages and will offer new ways of understanding this key research a

This is a thorough exploration of the models and methods of financial econometrics by one of the world's leading financial econometricians and is for students in economics, finance, statistics, mathematics, and engineering who are interested in financial applications. Based on courses taught around the world, the up-to-date content covers developments in econometrics and finance over the last twenty years while ensuring a solid grounding in the fundamental principles of the field. Care has been taken to link theory and application to provide real-world context for students. Worked exercises and empirical examples have also been included to make sure complicated concepts are solidly explained and understood.

This is a thorough exploration of the models and methods of financial econometrics by one of the world's leading financial econometricians and is for students in economics, finance, statistics, mathematics, and engineering who are interested in financial applications. Based on courses taught around the world, the up-to-date content covers developments in econometrics and finance over the last twenty years while ensuring a solid grounding in the fundamental principles of the field. Care has been taken to link theory and application to provide real-world context for students. Worked exercises and empirical examples have also been included to make sure complicated concepts are solidly explained and understood.

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and ad

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

Loss Models contains a wealth of examples that highlight the real-world applications of the concepts presented, and puts the emphasis on calculations and spreadsheet implementation. With a focus on the loss process, the book reviews the essential quantitative techniques such as random variables, basic distributional quantities, and the recursive method, and discusses techniques for classifying and creating distributions. Parametric, non-parametric, and Bayesian estimation methods are thoroughly covered. In addition, the authors offer practical advice for choosing an appropriate model. This important text:•Presents a revised and updated edition of the classic guide for actuaries that aligns with newly introduced Exams STAM and LTAM•Contains a wealth of exercises taken from previous exams•Includes fresh and additional content related to the material required by the Society of Actuaries (SOA) and the Canadian Institute of Actuaries (CIA)•Offers a solutions manual available for further ins

This book addresses essential questions about housing by building theoretical models based on various real world problems in Japan and testing these models using econometric methods. Almost all relate

By the award-winning former president of the Linguistic Society of America, this collection of some of John Russell Rickford's pioneering works shows how linguists in sociolinguistics and creole studies can benefit from utilizing data, theories and methods from each other, as they more frequently did in the 1960s and 1970s, when both subfields, in their modern forms at least, were getting started. The volume addresses fundamental sociolinguistic topics such as social class, style, fieldwork, speech community, sociolinguistic competence and language attitudes with data from Guyanese and other Caribbean creoles. Recurrent concepts are also considered including language versatility, variation and change, vernacular use, school success and criminal justice in African America and the Caribbean, using models, case studies and methodologies from sociolinguistics. Theoretical and applied scholars, students apprehensive about sociolinguistic fieldwork, and those considering dynamic methods like

This book presents a quantitative history of constitutional law in the United States and brings together humanistic and social-scientific approaches to studying law. Using theoretical models of adjudication, Tom S. Clark presents a statistical model of law and uses the model to document the historical development of constitutional law. Using sophisticated statistical methods and historical analysis of court decisions, the author documents how social and political forces shape the path of law. Spanning the history of constitutional law since Reconstruction, this book illustrates the way in which the law evolves with American life and argues that a social-scientific approach to the history of law illuminates connections across disparate areas of the law, connected by the social context in which the Constitution has been interpreted.

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.

This pioneering, course-tested text is the first to combine communications theory with the physics of optical communications. Comprehensive and rigorous, it brings together an in-depth treatment of the physical characteristics of the guided lightwave channel with the study of modern methods of algorithmic-based communication in time and space. The many different levels at which a lightwave communication signal can be described are integrated to provide a unified explanation of how a commonplace bit stream is transformed into a physical lightwave, how that lightwave travels through an optical fiber, and how it is then transformed back into the bit stream. Background fundamentals such as linear systems and electromagnetics are explained in relation to modern topics such as channel models, encoding, modulation and interference, and end-of-chapter problems are provided throughout. This is an essential text for students taking courses on optical communications, as well as researchers and pr

The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.

For courses in Introductory Statistics. Encourages statistical thinking using technology, innovative methods, and a sense of humor Inspired by the 2016 GAISE Report revision, Stats: Data and Models, 5th Edition by De Veaux/Velleman/Bock uses innovative strategies to help students think critically about data — while maintaining the book’s core concepts, coverage, and most importantly, readability.By using technology and simulations to demonstrate variability at critical points throughout the course, the authors make it easier for instructors to teach and for students to understand more complicated statistical concepts later in the course (such as the Central Limit Theorem). In addition, students get more exposure to large data sets and multivariate thinking, which better prepares them to be critical consumers of statistics in the 21st century. The 5th Edition’s approach to teaching Stats: Data and Models is revolutionary, yet it retains the book's lively tone

Richly illustrated throughout with actual tissue images, this innovative book shows that the soft-hard tissue junction is best understood in a biomechanical context. The authors describe their pioneering experimental methods, providing an essential structure-function framework for computational modelling, and thereby encouraging the development of more realistic, predictive models of this important tissue junction. Covering the three main musculoskeletal junctions of cartilage-bone, disc-vertebra, and ligament/tendon-bone, the relevant soft tissues are examined with respect to both their own inherent structure and their mode of integration with the hard tissue. The soft-hard tissue interface is explored with a focus on structural damage resulting from overloading, and its associated pathologies. Adopting a multiscale approach, ranging in structural resolution from the macro to fibril levels, this is a must-have guide to the field and an ideal resource for researchers seeking new and cr

Geodynamics is the study of the deformation and flow of the solid Earth and other planetary interiors. Focusing on the Earth's mantle, this book provides a comprehensive, mathematically advanced treatment of the continuum mechanics of mantle processes and the craft of formulating geodynamical models to approximate them. Topics covered include slow viscous flow, elasticity and viscoelasticity, boundary-layer theory, long-wave theories including lubrication theory and shell theory, two-phase flow, and hydrodynamic stability and thermal convection. A unifying theme is the utility of powerful general methods (dimensional analysis, scaling analysis, and asymptotic analysis) that can be applied in many specific contexts. Featuring abundant exercises with worked solutions for graduate students and researchers, this book will make a useful resource for Earth scientists and applied mathematicians with an interest in mantle dynamics and geodynamics more broadly.

Understand the benefits of robust statistics for signal processing with this authoritative yet accessible text. The first ever book on the subject, it provides a comprehensive overview of the field, moving from fundamental theory through to important new results and recent advances. Topics covered include advanced robust methods for complex-valued data, robust covariance estimation, penalized regression models, dependent data, robust bootstrap, and tensors. Robustness issues are illustrated throughout using real-world examples and key algorithms are included in a MATLAB Robust Signal Processing Toolbox accompanying the book online, allowing the methods discussed to be easily applied and adapted to multiple practical situations. This unique resource provides a powerful tool for researchers and practitioners working in the field of signal processing.

Why in the early 1970s does The Times reject the idea of a national lottery, as rewarding luck not merit and effort, but warmly welcome one by the 1990s? Why in the 1970s do the Daily Mail's TV reviews address serious contemporary themes such as class- and race-relations, whereas forty years later they are largely concerned with celebrities, talent shows, and nostalgia? Why does the Conservative Chancellor in the 2010s mention 'Britain' so very often, when the Conservative Chancellor in the 1970s scarcely does at all? Covering news stories spanning fort-five years, Michael Toolan explores how wealth inequality has been presented in centre-right British newspapers, focusing on changes in the representation may have helped present-day inequality seem justifiable. Toolan employs corpus linguistic and critical discourse analytic methods to identify changing lexis and verbal patterns and gaps, all of which contribute to the way wealth inequality was represented in each of the decades from t

This conference proceedings volume presents advanced methods in time series estimation models that are applicable various areas of applied economic research such as international economics, macroecono

This book introduces double-prism multi-mode scanning theory and technology, focusing on double Risley-prism, multi-mode scanning models, methods and key techniques applied in multi-mode optical scann

Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of Itô calculus, the central theorems in the field, and such approximation schemes as stochastic Runge–Kutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book's practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-

Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of Itô calculus, the central theorems in the field, and such approximation schemes as stochastic Runge–Kutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book's practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-

This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques.

The Euler and Navier–Stokes equations are the fundamental mathematical models of fluid mechanics, and their study remains central in the modern theory of partial differential equations. This volume of articles, derived from the workshop 'PDEs in Fluid Mechanics' held at the University of Warwick in 2016, serves to consolidate, survey and further advance research in this area. It contains reviews of recent progress and classical results, as well as cutting-edge research articles. Topics include Onsager's conjecture for energy conservation in the Euler equations, weak-strong uniqueness in fluid models and several chapters address the Navier–Stokes equations directly; in particular, a retelling of Leray's formative 1934 paper in modern mathematical language. The book also covers more general PDE methods with applications in fluid mechanics and beyond. This collection will serve as a helpful overview of current research for graduate students new to the area and for more established researc

This volume provides methods to study bone morphogenetic proteins (BMPs) and BMP modulators in cell culture, in both invertebrate and vertebrate animal models, and for therapeutic applications. Chapte

This book presents the latest developments in optimization and optimal control models; exact, approximate and hybrid methods; and their applications in lean and green supply chains. It examines supply

This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques.

Optimization methods play a central role in financial modeling. This textbook is devoted to explaining how state-of-the-art optimization theory, algorithms, and software can be used to efficiently solve problems in computational finance. It discusses some classical mean–variance portfolio optimization models as well as more modern developments such as models for optimal trade execution and dynamic portfolio allocation with transaction costs and taxes. Chapters discussing the theory and efficient solution methods for the main classes of optimization problems alternate with chapters discussing their use in the modeling and solution of central problems in mathematical finance. This book will be interesting and useful for students, academics, and practitioners with a background in mathematics, operations research, or financial engineering. The second edition includes new examples and exercises as well as a more detailed discussion of mean–variance optimization, multi-period models, and add

For three centuries, a mixture of religion, violence, and economic conditions created a fertile matrix in Western Europe that racialized an entire diasporic population who lived in the urban centers of the Latin West: Jews. This Element explores how religion and violence, visited on Jewish bodies and Jewish lives, coalesced to create the first racial state in the history of the West. It is an example of how the methods and conceptual frames of postcolonial and race studies, when applied to the study of religion, can be productive of scholarship that rewrites the foundational history of the past.

This book offers an up-to-date, comprehensive coverage of stochastic dominance and its related concepts in a unified framework. A method for ordering probability distributions, stochastic dominance has grown in importance recently as a way to measure comparisons in welfare economics, inequality studies, health economics, insurance wages, and trade patterns. Whang pays particular attention to inferential methods and applications, citing and summarizing various empirical studies in order to relate the econometric methods with real applications and using computer codes to enable the practical implementation of these methods. Intuitive explanations throughout the book ensure that readers understand the basic technical tools of stochastic dominance.

It is difficult to imagine forces in the modern world as potent as nationalism and religion. Both provide people with a source of meaning, each has motivated individuals to carry out extraordinary acts of heroism and cruelty, and both serve as the foundation for communal and personal identity. While the subject has received both scholarly and popular attention, this distinctive book is the first comparative study to examine the origins and development of three distinct models: religious nationalism, secular nationalism, and civil-religious nationalism. Using multiple methods, the authors develop a new theoretical framework that can be applied across diverse countries and religious traditions to understand the emergence, development, and stability of different church-state arrangements over time. The work combines public opinion, constitutional, and content analysis of the United States, Israel, India, Greece, Uruguay, and Malaysia, weaving together historical and contemporary illustrat

It is difficult to imagine forces in the modern world as potent as nationalism and religion. Both provide people with a source of meaning, each has motivated individuals to carry out extraordinary acts of heroism and cruelty, and both serve as the foundation for communal and personal identity. While the subject has received both scholarly and popular attention, this distinctive book is the first comparative study to examine the origins and development of three distinct models: religious nationalism, secular nationalism, and civil-religious nationalism. Using multiple methods, the authors develop a new theoretical framework that can be applied across diverse countries and religious traditions to understand the emergence, development, and stability of different church-state arrangements over time. The work combines public opinion, constitutional, and content analysis of the United States, Israel, India, Greece, Uruguay, and Malaysia, weaving together historical and contemporary illustrat

Working with Dynamic Crop Models: Methods, Tools and Examples for Agriculture and Environment, 3e, is a complete guide to working with dynamic system models, with emphasis on models in agronomy and en

High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clust

Born of a belief that economic insights should not require much mathematical sophistication, this book proposes novel and parsimonious methods to incorporate ignorance and uncertainty into economic modeling, without complex mathematics. Economics has made great strides over the past several decades in modeling agents' decisions when they are incompletely informed, but many economists believe that there are aspects of these models that are less than satisfactory. Among the concerns are that ignorance is not captured well in most models, that agents' presumed cognitive ability is implausible, and that derived optimal behavior is sometimes driven by the fine details of the model rather than the underlying economics. Compte and Postlewaite lay out a tractable way to address these concerns, and to incorporate plausible limitations on agents' sophistication. A central aspect of the proposed methodology is to restrict the strategies assumed available to agents.

Social scientists who study religion generally believe that social networks play a central role in religious life. However, most studies draw on measures that are relatively poor proxies for capturing the effects of social networks. This book illustrates how researchers can draw on formal social network analysis methods to explore the interplay of networks and religion. The book's introductory chapters provide overviews of the social scientific study of religion and social network analysis. The remaining chapters explore a variety of topics current in the social scientific study of religion, as well as introducing a variety of social network theories and methods, such as balance theory, ego-network analysis, exponential random graph models, and stochastic actor-oriented models. By embedding social network analysis within a social scientific study of religion framework, Networks and Religion offers an array of approaches for studying the role that social networks play in religious belie

Born of a belief that economic insights should not require much mathematical sophistication, this book proposes novel and parsimonious methods to incorporate ignorance and uncertainty into economic modeling, without complex mathematics. Economics has made great strides over the past several decades in modeling agents' decisions when they are incompletely informed, but many economists believe that there are aspects of these models that are less than satisfactory. Among the concerns are that ignorance is not captured well in most models, that agents' presumed cognitive ability is implausible, and that derived optimal behavior is sometimes driven by the fine details of the model rather than the underlying economics. Compte and Postlewaite lay out a tractable way to address these concerns, and to incorporate plausible limitations on agents' sophistication. A central aspect of the proposed methodology is to restrict the strategies assumed available to agents.

Demystifying Numerical Models: Step-by Step Modeling of Engineering Systems is the perfect guide on the analytic concepts of engineering components and systems. In simplified terms, the book focuses o

These proceedings provide methods, techniques, different mathematical tools and recent results in the study of formal and analytic solutions to Diff. (differential, partial differential, difference, q