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A non-technical introduction to the question of modeling with time-varying parameters, using the beta coefficient from Financial Economics as the main example. After a brief introduction to this coefficient for those not versed in finance, the book presents a number of rather well known tests for constant coefficients and then performs these tests on data from the Stockholm Exchange. The Kalman filter is then introduced and a simple example is used to demonstrate the power of the filter. The filter is then used to estimate the market model with time-varying betas. The book concludes with further examples of how the Kalman filter may be used in estimation models used in analyzing other aspects of finance. Since both the programs and the data used in the book are available for downloading, the book is especially valuable for students and other researchers interested in learning the art of modeling with time varying coefficients.

Unread book in perfect condition.

State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover: An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.

Dwarfs your fear towards complicated mathematical derivations and proofs. Experience Kalman filter with hands-on examples to grasp the essence. A book long awaited by anyone who could not dare to put their first step into Kalman filter. The author presents Kalman filter and other useful filters without complicated mathematical derivation and proof but with hands-on examples in MATLAB that will guide you step-by-step. The book starts with recursive filter and basics of Kalman filter, and gradually expands to application for nonlinear systems through extended and unscented Kalman filters. Also, some topics on frequency analysis including complementary filter are covered. Each chapter is balanced with theoretical background for absolute beginners and practical MATLAB examples to experience the principles explained. Once grabbing the book, you will notice it is not fearful but even enjoyable to learn Kalman filter.

Explains how Kalman filters are used to estimate the instantaneous state of a linear dynamic system, and covers random processes, stochastic systems, and nonlinear applications

A non-technical introduction to the question of modeling with time-varying parameters, using the beta coefficient from Financial Economics as the main example. After a brief introduction to this coefficient for those not versed in finance, the book presents a number of rather well known tests for constant coefficients and then performs these tests on data from the Stockholm Exchange. The Kalman filter is then introduced and a simple example is used to demonstrate the power of the filter. The filter is then used to estimate the market model with time-varying betas. The book concludes with further examples of how the Kalman filter may be used in estimation models used in analyzing other aspects of finance. Since both the programs and the data used in the book are available for downloading, the book is especially valuable for students and other researchers interested in learning the art of modeling with time varying coefficients.

Good condition. This is the average used book, that has all pages or leaves present, but may include writing. Book may be ex-library with stamps and stickers. 1.68

Explains how Kalman filters are used to estimate the instantaneous state of a linear dynamic system, and covers random processes, stochastic systems, and nonlinear applications

Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 2.35

Dwarfs your fear towards complicated mathematical derivations and proofs. Experience Kalman filter with hands-on examples to grasp the essence. A book long awaited by anyone who could not dare to put their first step into Kalman filter. The author presents Kalman filter and other useful filters without complicated mathematical derivation and proof but with hands-on examples in MATLAB that will guide you step-by-step. The book starts with recursive filter and basics of Kalman filter, and gradually expands to application for nonlinear systems through extended and unscented Kalman filters. Also, some topics on frequency analysis including complementary filter are covered. Each chapter is balanced with theoretical background for absolute beginners and practical MATLAB examples to experience the principles explained. Once grabbing the book, you will notice it is not fearful but even enjoyable to learn Kalman filter.

Kalman Filtering This book provides readers with a solid introduction to the theoretical and practical aspects of Kalman filtering. It has been updated with the latest developments in the implementation and application of Kalman filtering, including adaptations for nonlinear filtering, more robust smoothing methods, and developing applications in navigation. Full description

State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover: An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.

Tibor Kalman: Perverse Optimist is the definitive and exuberant document of the late Tibor Kalman's work and ideas. This full-color, oversize title reveals Kalman's thoughts on magazines, advertising, sex, bookstores, food, and the design profession. Product designs, stills and storyboards from his film and video projects, and spreads from his book and magazine work are included. The impressive list of contributors includes Kurt Andersen, Paola Antonelli, David Byrne, Jay Chiat, Steven Heller, Isaac Mizrahi, Chee Pearlman, Rick Poynor, and Ingrid Sischy.

This book provides a complete explanation of estimation theory and application, modeling approaches, and model evaluation. Each topic starts with a clear explanation of the theory (often including historical context), followed by application issues that should be considered in the design. Different implementations designed to address specific problems are presented, and numerous examples of varying complexity are used to demonstrate the concepts.This book is intended primarily as a handbook for engineers who must design practical systems.  Its primary goal is to explain all important aspects of Kalman filtering and least-squares theory and application.  Discussion of estimator design and model development is emphasized so that the reader may develop an estimator that meets all application requirements and is robust to modeling assumptions.  Since it is sometimes difficult to a priori determine the best model structure, use of exploratory data analysis to define model structure is discussed.  Methods for deciding on the "best" model are also presented. A second goal is to present little known extensions of least squares estimation or Kalman filtering that provide guidance on model structure and parameters, or make the estimator more robust to changes in real-world behavior.A third goal is discussion of implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared.The fourth goal is to provide the designer/analyst with guidance in evaluating estimator performance and in determining/correcting problems.The final goal is to provide a subroutine library that simplifies implementation, and flexible general purpose high-level drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering.

A unique, easy-to-use guide to radar tracking and Kalmanfiltering This book presents the first truly accessible treatment of radartracking; Kalman, Swerling, and Bayes filters for linear andnonlinear ballistic and satellite tracking systems; and thevoltage-processing methods (Givens, Householder, and Gram-Schmidt)for least-squares filtering to correct for computer round-offerrors. Tracking and Kalman Filtering Made Easy emphasizes thephysical and geometric aspects of radar filters as well as thebeauty and simplicity of their mathematics. An abundance of designequations, procedures, and curves allows readers to design trackingfilters quickly and test their performance using only a pocketcalculator! The text incorporates problems and solutions, figures andphotographs, and astonishingly simple derivations for variousfilters. It tackles problems involving clutter returns, redundanttarget detections, inconsistent data, track-start and track-droprules, data association, matched filtering, tracking with chirpwaveform, and more. The book also covers useful techniques such asthe moving target detector (MTD) clutter rejection technique. Allexplanations are given in clear and simple terms, including: * The voltage-processing approach to least-squares filtering * The correlation between such procedures as discrete orthogonalLegendre polynomial (DOLP) and voltage processing * The mathematical sameness of tracking and estimation problems onthe one hand, and sidelobe canceling and adaptive array processingon the other * The massively parallel systolic array sidelobe canceler processor * Important computational accuracy issues * An appended comparison between the Kalman and the Swerlingfilters, written by Dr. Peter Swerling Tracking and Kalman Filtering Made Easy is invaluable forengineers, scientists, and mathematicians involved in trackingfilter design. Its straightforward approach makes it an excellenttextbook for senior-undergraduate and first-year graduate courses.

This book provides a complete explanation of estimation theory and application, modeling approaches, and model evaluation. Each topic starts with a clear explanation of the theory (often including historical context), followed by application issues that should be considered in the design. Different implementations designed to address specific problems are presented, and numerous examples of varying complexity are used to demonstrate the concepts.This book is intended primarily as a handbook for engineers who must design practical systems.  Its primary goal is to explain all important aspects of Kalman filtering and least-squares theory and application.  Discussion of estimator design and model development is emphasized so that the reader may develop an estimator that meets all application requirements and is robust to modeling assumptions.  Since it is sometimes difficult to a priori determine the best model structure, use of exploratory data analysis to define model structure is discussed.  Methods for deciding on the "best" model are also presented. A second goal is to present little known extensions of least squares estimation or Kalman filtering that provide guidance on model structure and parameters, or make the estimator more robust to changes in real-world behavior.A third goal is discussion of implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared.The fourth goal is to provide the designer/analyst with guidance in evaluating estimator performance and in determining/correcting problems.The final goal is to provide a subroutine library that simplifies implementation, and flexible general purpose high-level drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering.

Tibor Kalman, probably best known for the witty designs of his company M&Co and his provocative work for Benetton's Colors magazine, defines the eclectic multidisciplinary approach that has come to characterize graphic design in the past decade. Eclectic is perhaps an understatement: Kalman's work ranges from journalism, advertising, and publishing to watches, paper weights, rulers, album covers, t-shirts, film titles, commercials, urban guidelines, and more. Born in Budapest in 1949, Kalman emigrated to the U.S. in 1956. He soon began working at Barnes & Noble, where he later became design director; he subsequently worked as art director at Artforum and Interview. His international acclaim came largely as a result of his work at M&Co, the trend-setting design firm he founded in New York City in 1979 and continues to run. Tibor, designed by Michael Bierut of Pentagram and edited by I.D. Magazine senior writer Peter Hall, is the first comprehensive collection of Kalman's work and ideas. This full-color title-numbering over 400 pages-includes a pictorial manifesto by Kalman, revealing his thoughts on magazines, advertising, sex, bookstores, food, and the design profession. Product designs, stills and storyboards from his film and video projects, and spreads from his book and magazine work are included, creating what Kalman calls "an almanac of oddities." An impressive list of essay contributors includes Steven Heller, David Byrne, Jay Chiat, Kurt Andersen, Paola Antonelli, Isaac Mizrahi, Ingrid Sischy, Chee Pearlman, and Rick Poynor.

In 2008 the National Academy of Engineering awarded Rudolf Kalman the Charles Stark Draper Prize--the engineering equivalent of the Nobel Prize -- for the development and dissemination of the optimal digital technique (known as the Kalman Filter) that is pervasively used to control a vast array of consumer, health, commercial, and defense products. Fundamentals of Kalman Filtering, Fourth Edition is a practical guide to building Kalman filters that shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. For this edition, source code listings appearing in the text have been converted from FORTRAN to MATLAB(R). In addition, both FORTRAN and MATLAB* source code are available electronically for all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text. In certain instances, the authors intentionally introduce mistakes to the initial filter designs to show the reader what happens when the filter is not working properly. The text carefully sets up a problem before the Kalman filter is actually formulated, to give the reader an intuitive feel for the problem being addressed. Because real problems are seldom presented as differential equations, and usually do not have unique solutions, the authors illustrate several different filtering approaches. Readers will gain experience in software and performance tradeoffs for determining the best filtering approach. The fourth edition features four new chapters presenting the following techniques: the State Dependent Ricatti Equation Filter (SDRE), the Unscented Kalman Filter (UKF), the Interactive Multiple Model (IMM) Filter Bank, and the Cramer-Rao lower bound (CRLB), for finding the best that a filter can perform.

Tibor Kalman, probably best known for the witty designs of his company M&Co and his provocative work for Benetton's Colors magazine, defines the eclectic multidisciplinary approach that has come to characterize graphic design in the past decade. Eclectic is perhaps an understatement: Kalman's work ranges from journalism, advertising, and publishing to watches, paper weights, rulers, album covers, t-shirts, film titles, commercials, urban guidelines, and more. Born in Budapest in 1949, Kalman emigrated to the U.S. in 1956. He soon began working at Barnes & Noble, where he later became design director; he subsequently worked as art director at Artforum and Interview. His international acclaim came largely as a result of his work at M&Co, the trend-setting design firm he founded in New York City in 1979 and continues to run. Tibor, designed by Michael Bierut of Pentagram and edited by I.D. Magazine senior writer Peter Hall, is the first comprehensive collection of Kalman's work and ideas. This full-color title-numbering over 400 pages-includes a pictorial manifesto by Kalman, revealing his thoughts on magazines, advertising, sex, bookstores, food, and the design profession. Product designs, stills and storyboards from his film and video projects, and spreads from his book and magazine work are included, creating what Kalman calls "an almanac of oddities." An impressive list of essay contributors includes Steven Heller, David Byrne, Jay Chiat, Kurt Andersen, Paola Antonelli, Isaac Mizrahi, Ingrid Sischy, Chee Pearlman, and Rick Poynor.

From children's books to Germanyer covers, fashion to philosophical musings, this first retrospective book on the beloved illustrator, author, and designer Maira Kalman is an inventory of imaginative genius certain to delight her many fans. The world as seen through Kalman's eyes is a quirky, slightly off-kilter place as colourful and varied as a kaleidoscope. For decades this brilliant artist has captured our hearts with her whimsical illustrations and engaged our minds with her trenchant observations. A companion to a travelling exhibition, this monograph on Kalman's work features hundreds of paintings, drawings, sketchbook pages, and journal entries as well as rarely glimpsed photographs, stills from performance pieces, and examples of her newest project, embroidery. Kalman was born in Tel Aviv in 1949 and moved to Germany at the age of four. Among her varied body of work are illustrated books for children and adults, clocks she designed with her late husband Tibor Kalman, columns for The Germany Times, fabrics for Maharam and Isaac Mizrahi, and sets for the choreographer Mark Morris. In this book, Kalman offers commentary on her life as an artist, collector, observer, traveller, and maker of lists, while essays by curator Ingrid Schaffner and art historian Kenneth Silver explore her unique gift for distilling the extraordinary from the merely ordinary. From the youngest readers to the most discerning critics, Kalman's many admirers will embrace this wonderful celebration of a life dedicated to making art.