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Linear Estimation (Anglais) Broché – 31 mars 2000

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Descriptions du produit

Présentation de l'éditeur

This textbook is intended for a graduate-level course and assumes familiarity with basic concepts from matrix theory, linear algebra, and linear system theory. Six appendices at the end of the book provide the reader with enough background and review material in all these areas.

This original work offers the most comprehensive and up-to-date treatment of the important subject of optimal linear estimation, which is encountered in many areas of engineering such as communications, control, and signal processing, and also in several other fields, e.g., econometrics and statistics. The book not only highlights the most significant contributions to this field during the 20th century, including the works of Wiener and Kalman, but it does so in an original and novel manner that paves the way for further developments in the new millennium. This book contains a large collection of problems that complement the text and are an important part of it, in addition to numerous sections that offer interesting historical accounts and insights. The book also includes several results that appear in print for the first time.

Quatrième de couverture

This original work offers the most comprehensive and up-to-date treatment of the important subject of optimal linear estimation, which is encountered in many areas of engineering such as communications, control, and signal processing, and also in several other fields, e.g., econometrics and statistics. The book not only highlights the most significant contributions to this field during the 20th century, including the works of Wiener and Kalman, but it does so in an original and novel manner that paves the way for further developments. This book contains a large collection of problems that complement it and are an important part of piece, in addition to numerous sections that offer interesting historical accounts and insights. The book also includes several results that appear in print for the first time.

FEATURES/BENEFITS

  • Takes a geometric point of view.
  • Emphasis on the numerically favored array forms of many algorithms.
  • Emphasis on equivalence and duality concepts for the solution of several related problems in adaptive filtering, estimation, and control.
    • These features are generally absent in most prior treatments, ostensibly on the grounds that they are too abstract and complicated. It is the authors' hope that these misconceptions will be dispelled by the presentation herein, and that the fundamental simplicity and power of these ideas will be more widely recognized and exploited. Among other things, these features already yielded new insights and new results for linear and nonlinear problems in areas such as adaptive filtering, quadratic control, and estimation, including the recent Hà theories.

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Amazon.com: 4.3 étoiles sur 5 6 commentaires
1 internautes sur 1 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 linear estimation 28 juin 2013
Par ümit aydeniz - Publié sur Amazon.com
Format: Broché Achat vérifié
especially i needed to understand kalman filtering and state space aproach to the linear estimation theory. At this point it satisfied my needs.
2 internautes sur 3 ont trouvé ce commentaire utile 
1.0 étoiles sur 5 very poorly printed book. 12 novembre 2013
Par just_me - Publié sur Amazon.com
Format: Broché Achat vérifié
My review is not about the content of the book but its printing.
The printing is just a photocopy and the binding is very poor.
I think the book will split into many pieces just within a few days.
The book is very expensive but then how can the printing be so poor?
I am totally shocked!
Never ever buy this book! This is my suggestion. Just take a photocopy from library and you will
have the same book and will profit at least 20 times!
14 internautes sur 15 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Wonderful and insightful 17 septembre 2001
Par Nicolas Chapados - Publié sur Amazon.com
Format: Broché
This is one of the best engineering textbooks I have read, period. Although the subject matter is not for the faint-hearted, the authors' attention to pedagogical details shine throughout (repetition is the key to learning). The Kalman filter is introduced naturally as a consequence of a general framework for obtaining the best linear estimator of a random variable given others (earlier observations), and the geometric intuition is stressed repeatedly.
No important issue is omitted, including a very complete treatment of numerical issues and fast algorithms. My only gripe is with the assumption that all model parameters are KNOWN; in other words, the important aspect system identification (parameter estimation, learning, or whatever you call it in your field) is left to other textbooks.
Moreover, and no minor accomplishment, is the amazingly small number of typographical errors (at least up to where I have read so far; a bit over half the book), which is remarkable given the dense mathematical contents.
All in all, I would give it 6 stars if possible. Everything is there: it transmits a deep intuition for the matter, a places it in its historical context through interesting and amusing notes; it leaves the reader fulfilled but not overwhelmed.
19 internautes sur 20 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Linear Estimation from A to Z. 6 février 2001
Par Jeffrey Andrews - Publié sur Amazon.com
Format: Broché
Kailath, Sayed, and Hassibi do an excellent job of explaining what is a fairly complicated subject. This book is best-suited for scholars who desire a deep understanding of estimation theory. Engineers who want to quickly understand how to implement a Kalman Filter might be better off buying Adaptive Filter Theory by Simon Haykin.
The first chapter provides a good overview of the book, although it makes the most sense once the subject matter of the rest of the book has been digested a bit. A consistent framework emphasizing innovations (or the new information which appears at any iteration) is used throughout the book, and both continuous and discrete-time techniques for stochastic estimation are given nearly equal treatment, although the real-world engineer is likely to be interested in the latter.
Professor Kailath's articulate nature and knack for the clever anecdote or one-liner shines throughout the book, making it, while very mathematical in nature, quite readable for the motivated student.
5 internautes sur 5 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Excellent text 25 septembre 2005
Par Shadow - Publié sur Amazon.com
Format: Broché
This is an excellent text that covers estimation theory from a modern point of view. It will be especially interesting to anyone with a graduate degree in physics because Kailath, et al derive the theory of linear estimation from a point of view very similar to that of modern quantum mechanics - they even use similar bra/ket notation!

Basic and advanced statistical mathematics is somewhat an implied prerequisite for understanding this text. From what I have seen, I honestly find nothing negative to critique - its probably one of the best technical textbooks I have in my large library.
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