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Random Iterative Models
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Book Synopsis Random Iterative Models by : Marie Duflo
Download or read book Random Iterative Models written by Marie Duflo and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, self-contained review of a wide range of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, organizing multi-access broadcast channels, self-learning of neural networks ...). Suitable for mathematicians (researchers and also students) and engineers.
Book Synopsis Iterative and Non-iterative Approaches to Estimating Random Coefficient Regression Parameters by : Mary Jacqueline York
Download or read book Iterative and Non-iterative Approaches to Estimating Random Coefficient Regression Parameters written by Mary Jacqueline York and published by . This book was released on 1998 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multilevel Modeling by : Steven P. Reise
Download or read book Multilevel Modeling written by Steven P. Reise and published by Psychology Press. This book was released on 2003-01-30 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book illustrates the current work of leading multilevel modeling (MLM) researchers from around the world. The book's goal is to critically examine the real problems that occur when trying to use MLMs in applied research, such as power, experimental design, and model violations. This presentation of cutting-edge work and statistical innovations in multilevel modeling includes topics such as growth modeling, repeated measures analysis, nonlinear modeling, outlier detection, and meta analysis. This volume will be beneficial for researchers with advanced statistical training and extensive experience in applying multilevel models, especially in the areas of education; clinical intervention; social, developmental and health psychology, and other behavioral sciences; or as a supplement for an introductory graduate-level course.
Book Synopsis Iterative Detection by : Keith Chugg
Download or read book Iterative Detection written by Keith Chugg and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Iterative Detection: Adaptivity, Complexity Reduction, and Applications is a primary resource for both researchers and teachers in the field of communication. Unlike other books in the area, it presents a general view of iterative detection that does not rely heavily on coding theory or graph theory. The features of the text include: Both theoretical background and numerous real-world applications. Over 70 detailed examples, 100 problems, 180 illustrations, tables of notation and acronyms, and an extensive bibliography and subject index. A whole chapter devoted to a case study on turbo decoder design. Receiver design guidelines, rules and suggestions. The most advanced view of iterative (turbo) detection based only on block diagrams and standard detection and estimation theory. Development of adaptive iterative detection theory. Application of adaptive iterative detection to phase and channel tracking in turbo coded systems and systems representative of digital mobile radio designs. An entire chapter dedicated to complexity reduction. Numerous recent research results. Discussion of open problems at the end of each chapter. Among the applications considered in this book are joint equalization and decoding, turbo codes, multiuser detection and decoding, broadband wireless channel equalization, and applications to two-dimensional storage and imaging systems. Audience: Iterative Detection: Adaptivity, Complexity Reduction, and Applications provides an accessible and detailed reference for researchers, practicing engineers, and students working in the field of detection and estimation. It will be of particular interest to those who would like to learn how iterative detection can be applied to equalization, interference mitigation, and general signal processing tasks. Researchers and practicing engineers interested in learning the turbo decoding algorithm should also have this book.
Book Synopsis The Random-Cluster Model by : Geoffrey R. Grimmett
Download or read book The Random-Cluster Model written by Geoffrey R. Grimmett and published by Springer Science & Business Media. This book was released on 2006-12-13 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The random-cluster model has emerged as a key tool in the mathematical study of ferromagnetism. It may be viewed as an extension of percolation to include Ising and Potts models, and its analysis is a mix of arguments from probability and geometry. The Random-Cluster Model contains accounts of the subcritical and supercritical phases, together with clear statements of important open problems. The book includes treatment of the first-order (discontinuous) phase transition.
Book Synopsis Probabilistic Methods In Fluids, Proceedings Of The Swansea 2002 Workshop by : Ian M Davies
Download or read book Probabilistic Methods In Fluids, Proceedings Of The Swansea 2002 Workshop written by Ian M Davies and published by World Scientific. This book was released on 2003-06-13 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains recent research papers presented at the international workshop on “Probabilistic Methods in Fluids” held in Swansea. The central problems considered were turbulence and the Navier-Stokes equations but, as is now well known, these classical problems are deeply intertwined with modern studies of stochastic partial differential equations, jump processes and random dynamical systems. The volume provides a snapshot of current studies in a field where the applications range from the design of aircraft through the mathematics of finance to the study of fluids in porous media.
Book Synopsis Probabilistic Methods in Fluids by : Ian Malcolm Davies
Download or read book Probabilistic Methods in Fluids written by Ian Malcolm Davies and published by World Scientific. This book was released on 2003 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains recent research papers presented at the international workshop on ?Probabilistic Methods in Fluids? held in Swansea. The central problems considered were turbulence and the Navier-Stokes equations but, as is now well known, these classical problems are deeply intertwined with modern studies of stochastic partial differential equations, jump processes and random dynamical systems. The volume provides a snapshot of current studies in a field where the applications range from the design of aircraft through the mathematics of finance to the study of fluids in porous media.
Book Synopsis Monte Carlo Statistical Methods by : Christian Robert
Download or read book Monte Carlo Statistical Methods written by Christian Robert and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt: We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.
Book Synopsis Stochastic Controls by : Jiongmin Yong
Download or read book Stochastic Controls written by Jiongmin Yong and published by Springer Science & Business Media. This book was released on 1999-06-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging by : Ke Chen
Download or read book Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging written by Ke Chen and published by Springer Nature. This book was released on 2023-02-24 with total page 1981 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook gathers together the state of the art on mathematical models and algorithms for imaging and vision. Its emphasis lies on rigorous mathematical methods, which represent the optimal solutions to a class of imaging and vision problems, and on effective algorithms, which are necessary for the methods to be translated to practical use in various applications. Viewing discrete images as data sampled from functional surfaces enables the use of advanced tools from calculus, functions and calculus of variations, and nonlinear optimization, and provides the basis of high-resolution imaging through geometry and variational models. Besides, optimization naturally connects traditional model-driven approaches to the emerging data-driven approaches of machine and deep learning. No other framework can provide comparable accuracy and precision to imaging and vision. Written by leading researchers in imaging and vision, the chapters in this handbook all start with gentle introductions, which make this work accessible to graduate students. For newcomers to the field, the book provides a comprehensive and fast-track introduction to the content, to save time and get on with tackling new and emerging challenges. For researchers, exposure to the state of the art of research works leads to an overall view of the entire field so as to guide new research directions and avoid pitfalls in moving the field forward and looking into the next decades of imaging and information services. This work can greatly benefit graduate students, researchers, and practitioners in imaging and vision; applied mathematicians; medical imagers; engineers; and computer scientists.
Book Synopsis Bayesian Statistical Modelling by : Peter Congdon
Download or read book Bayesian Statistical Modelling written by Peter Congdon and published by John Wiley & Sons. This book was released on 2007-04-04 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology
Book Synopsis Iterative Learning Control with Passive Incomplete Information by : Dong Shen
Download or read book Iterative Learning Control with Passive Incomplete Information written by Dong Shen and published by Springer. This book was released on 2018-04-16 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an in-depth discussion of iterative learning control (ILC) with passive incomplete information, highlighting the incomplete input and output data resulting from practical factors such as data dropout, transmission disorder, communication delay, etc.—a cutting-edge topic in connection with the practical applications of ILC. It describes in detail three data dropout models: the random sequence model, Bernoulli variable model, and Markov chain model—for both linear and nonlinear stochastic systems. Further, it proposes and analyzes two major compensation algorithms for the incomplete data, namely, the intermittent update algorithm and successive update algorithm. Incomplete information environments include random data dropout, random communication delay, random iteration-varying lengths, and other communication constraints. With numerous intuitive figures to make the content more accessible, the book explores several potential solutions to this topic, ensuring that readers are not only introduced to the latest advances in ILC for systems with random factors, but also gain an in-depth understanding of the intrinsic relationship between incomplete information environments and essential tracking performance. It is a valuable resource for academics and engineers, as well as graduate students who are interested in learning about control, data-driven control, networked control systems, and related fields.
Book Synopsis Modeling Intraindividual Variability With Repeated Measures Data by : Scott L. Hershberger
Download or read book Modeling Intraindividual Variability With Repeated Measures Data written by Scott L. Hershberger and published by Psychology Press. This book was released on 2013-06-17 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines how individuals behave across time and to what degree that behavior changes, fluctuates, or remains stable. It features the most current methods on modeling repeated measures data as reported by a distinguished group of experts in the field. The goal is to make the latest techniques used to assess intraindividual variability accessible to a wide range of researchers. Each chapter is written in a "user-friendly" style such that even the "novice" data analyst can easily apply the techniques. Each chapter features: a minimum discussion of mathematical detail; an empirical example applying the technique; and a discussion of the software related to that technique. Content highlights include analysis of mixed, multi-level, structural equation, and categorical data models. It is ideal for researchers, professionals, and students working with repeated measures data from the social and behavioral sciences, business, or biological sciences.
Book Synopsis Stochastic Storage Processes by : N.U. Prabhu
Download or read book Stochastic Storage Processes written by N.U. Prabhu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained treatment of stochastic processes arising from models for queues, insurance risk, and dams and data communication, using their sample function properties. The approach is based on the fluctuation theory of random walks, L vy processes, and Markov-additive processes, in which Wiener-Hopf factorisation plays a central role. This second edition includes results for the virtual waiting time and queue length in single server queues, while the treatment of continuous time storage processes is thoroughly revised and simplified. With its prerequisite of a graduate-level course in probability and stochastic processes, this book can be used as a text for an advanced course on applied probability models.
Book Synopsis Geostatistical Reservoir Modeling by : Michael J. Pyrcz
Download or read book Geostatistical Reservoir Modeling written by Michael J. Pyrcz and published by Oxford University Press. This book was released on 2014-04-16 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Published in 2002, the first edition of Geostatistical Reservoir Modeling brought the practice of petroleum geostatistics into a coherent framework, focusing on tools, techniques, examples, and guidance. It emphasized the interaction between geophysicists, geologists, and engineers, and was received well by professionals, academics, and both graduate and undergraduate students. In this revised second edition, Deutsch collaborates with co-author Michael Pyrcz to provide an expanded (in coverage and format), full color illustrated, more comprehensive treatment of the subject with a full update on the latest tools, methods, practice, and research in the field of petroleum Geostatistics. Key geostatistical concepts such as integration of geologic data and concepts, scale considerations, and uncertainty models receive greater attention, and new comprehensive sections are provided on preliminary geological modeling concepts, data inventory, conceptual model, problem formulation, large scale modeling, multiple point-based simulation and event-based modeling. Geostatistical methods are extensively illustrated through enhanced schematics, work flows and examples with discussion on method capabilities and selection. For example, this expanded second edition includes extensive discussion on the process of moving from an inventory of data and concepts through conceptual model to problem formulation to solve practical reservoir problems. A greater number of examples are included, with a set of practical geostatistical studies developed to illustrate the steps from data analysis and cleaning to post-processing, and ranking. New methods, which have developed in the field since the publication of the first edition, are discussed, such as models for integration of diverse data sources, multiple point-based simulation, event-based simulation, spatial bootstrap and methods to summarize geostatistical realizations.
Book Synopsis Functional Coherence of Molecular Networks in Bioinformatics by : Mehmet Koyutürk
Download or read book Functional Coherence of Molecular Networks in Bioinformatics written by Mehmet Koyutürk and published by Springer Science & Business Media. This book was released on 2011-09-22 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Molecular networks provide descriptions of the organization of various biological processes, including cellular signaling, metabolism, and genetic regulation. Knowledge on molecular networks is commonly used for systems level analysis of biological function; research and method development in this area has grown tremendously in the past few years. This book will provide a detailed review of existing knowledge on the functional characterization of biological networks. In 15 chapters authored by an international group of prolific systems biology and bioinformatics researchers, it will organize, conceptualize, and summarize the existing core of research results and computational methods on understanding biological function from a network perspective.
Book Synopsis Handbook of Discrete-Valued Time Series by : Richard A. Davis
Download or read book Handbook of Discrete-Valued Time Series written by Richard A. Davis and published by CRC Press. This book was released on 2016-01-06 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model a Wide Range of Count Time Series Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed ca
