The Variational Bayes Method in Signal Processing, V?clav ?m?dl; Anthony Quinn
Автор: Jianzhong Wu Название: Variational Methods in Molecular Modeling ISBN: 9811025002 ISBN-13(EAN): 9789811025006 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book presents tutorial overviews for many applications of variational methods to molecular modeling. Topics discussed include the Gibbs-Bogoliubov-Feynman variational principle, square-gradient models, classical density functional theories, self-consistent-field theories, phase-field methods, Ginzburg-Landau and Helfrich-type phenomenological models, dynamical density functional theory, and variational Monte Carlo methods. Illustrative examples are given to facilitate understanding of the basic concepts and quantitative prediction of the properties and rich behavior of diverse many-body systems ranging from inhomogeneous fluids, electrolytes and ionic liquids in micropores, colloidal dispersions, liquid crystals, polymer blends, lipid membranes, microemulsions, magnetic materials and high-temperature superconductors.
All chapters are written by leading experts in the field and illustrated with tutorial examples for their practical applications to specific subjects. With emphasis placed on physical understanding rather than on rigorous mathematical derivations, the content is accessible to graduate students and researchers in the broad areas of materials science and engineering, chemistry, chemical and biomolecular engineering, applied mathematics, condensed-matter physics, without specific training in theoretical physics or calculus of variations.
Presents the Bayesian approach to statistical signal processing for a variety of useful model sets
This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on "Sequential Bayesian Detection," a new section on "Ensemble Kalman Filters" as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to "fill-in-the gaps" of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical "sanity testing" lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems.
The second edition of Bayesian Signal Processing features
"Classical" Kalman filtering for linear, linearized, and nonlinear systems; "modern" unscented and ensemble Kalman filters: and the "next-generation" Bayesian particle filters
Sequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems
Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics
New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving
MATLAB(R) notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available
Problem sets included to test readers' knowledge and help them put their new skills into practice Bayesian
Signal Processing, Second Edition is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.
Описание: This book presents a unique viewpoint of signal processing from the Bayesian perspective in contrast to the pure statistical approach found in many textbooks. It features the next generation of processors that have recently been enabled with the advent of high speed/high throughput computers. The emphasis is on nonlinear/non-Gaussian problems, but classical techniques are included as special cases to enable the reader familiar with such methods to draw a parallel between the approaches. The common ground is the model sets. This text brings the reader from the classical methods of model-based signal processing including Kalman filtering for linear, linearized and approximate nonlinear processors as well as the recently developed unscented or sigma-point filters to the next generation of processors that will clearly dominate the future of model-based signal processing for years to come. Current applications (e.g. structures, tracking, equalization, biomedical) and simple examples to motivate the organization of the text are discussed. Examples are given to motivate all of the models and prepare the reader for further developments in subsequent chapters. In each case the processor along with accompanying simulations are discussed and applied to various data sets demonstrating the applicability and power of the Bayesian approach. The proposed text will be linked to the MATLAB (signal processing standard software) software package providing Notes as well as simple coding examples for illustrative purposes.
Автор: Barber Название: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Издательство: Cambridge Academ Рейтинг: Цена: 11088.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.
Автор: Greenberg Название: Introduction to Bayesian Econometrics ISBN: 1107015316 ISBN-13(EAN): 9781107015319 Издательство: Cambridge Academ Рейтинг: Цена: 8078.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook is an introduction to econometrics from the Bayesian viewpoint. New material includes a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The R programming language is also emphasized.
Автор: Damien, Paul; Dellaportas, Petros; Polson, Nichola Название: Bayesian Theory and Applications ISBN: 0198739079 ISBN-13(EAN): 9780198739074 Издательство: Oxford Academ Рейтинг: Цена: 11088.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: No phenomenon in any aspect of human enterprise is known with certainty. Probability and Statistics help us quantify uncertainty and lead to better decisions that, hopefully, enhance life. The impact of the ideas in this book has already revolutionised our ability to take informed decisions, and continues to do so at an astonishing rate.
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