Steven M. Kay This text is geared towards a one-semester graduate-level course in statistical signal processing and estimation theory. The author balances technical detail with practical and implementation issues, delivering an exposition that is both theoretically rigorous and application-oriented. The book covers topics such as minimum variance unbiased estimators, the Cramer-Rao bound, best linear unbiased estimators, maximum likelihood estimation, recursive least squares, Bayesian estimation techniques, and the Wiener and Kalman filters. The author provides numerous examples, which illustrate both theory and applications for problems such as high-resolution spectral analysis, system identification, digital filter design, adaptive beamforming and noise cancellation, and tracking and localization. The primary audience will be those involved in the design and implementation of optimal estimation algorithms on digital computers. The text assumes that you have a background in probability and random processes and linear and matrix algebra and exposure to basic signal processing. Students as well as researchers and practicing engineers will find the text an invaluable introduction and resource for scalar and vector parameter estimation theory and a convenient reference for the design of successive parameter estimation algorithms.

Alberto Leon-Garcia Indra Widjaja

Theodore W. Gamelin Robert Everist Greene A fresh approach to introductory topology, this volume explains nontrivial applications of metric space topology to analysis, clearly establishing their relationship. Also, topics from elementary algebraic topology focus on concrete results with minimal algebraic formalism. The first two chapters consider metric space and point-set topology; the second two, algebraic topological material. 1983 edition. Solutions to Selected Exercises. List of Notations. Index. 51 illus.

Feng Zhao Leonidas Guibas

Stephane Mallat

Claude Berge An extraordinary variety of disciplines rely on graphs to convey their fundamentals as well as their finer points. With this concise and well-written text, anyone with a firm grasp of general mathematics can follow the development of graph theory and learn to apply its principles in methods both formal and abstract. The work of a distinguished mathematician, this text uses practical examples to illustrate the theory's broad range of applications, from the behavioral sciences, information theory, cybernetics, and other areas, to mathematical disciplines such as set and matrix theory. 1966 edition. 109 black-and-white illus.

N. Young This textbook is an introduction to the theory of Hilbert spaces and its applications. The notion of a Hilbert space is a central idea in functional analysis and can be used in numerous branches of pure and applied mathematics. Dr. Young stresses these applications particularly for the solution of partial differential equations in mathematical physics and to the approximation of functions in complex analysis. Some basic familiarity with real analysis, linear algebra and metric spaces is assumed, but otherwise the book is self-contained. The book is based on courses given at the University of Glasgow and contains numerous examples and exercises (many with solutions). The book will make an excellent first course in Hilbert space theory at either undergraduate or graduate level and will also be of interest to electrical engineers and physicists, particularly those involved in control theory and filter design.

James F. Kurose Keith W. Ross

T. Hastie R. Tibshirani J. H. Friedman During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes theimprtant ideas in these areas ina common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a vluable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learing (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

Peter J. Feibelman

Bill Watterson

Thomas M. Cover Joy A. Thomas

Simon Haykin

Tracy D Terrell Mary B Rogers Betsy J. Kerr Guy Spielmann

Tracy D. Terrell Mary B. Rogers Betsy J. Kerr Guy Spielmann Deux mondes, third edition is widely regarded as the most communicative text available for beginning French courses. Based on the Natural Approach, developed by Tracy Terrell and Stephen Krashen, Deux mondes, third edition offers a way of teaching beginning language students that helps them develop proficiency, especially in listening and speaking, and allows students to participate in real discussion at a very early stage. The Natural Approach in general, and Deux mondes, third edition in particular, present material inductively (so that the activities drive the grammar) and in the context of culturally rich themes and topics. Overall, the third edition of Deux mondes offers users a streamlined presentation, with less material on each page, somewhat less vocabulary per chapter, and shorter and clearer grammar explanations. Changes to the new edition include all new readings and culture notes, two new chapter themes, marginal annotations for the grammar sections, a clearer, more attractive design, and video integrated with the Cahier.

Bert Mendelson An undergraduate introduction to the fundamentals of topology — engagingly written, filled with helpful insights, complete with many stimulating and imaginative exercises to help students develop a solid grasp of the subject.

Judith Muyskens Linda Harlow Michèle Vialet Jean-François Brière All students are not equal when it comes to the intermediate course?they all come to class with different levels of language preparation. It was because of this challenge that BRAVO! was created?and over the years it has evolved into a proven solution for student success. BRAVO! brings parity to the intermediate course, enabling students of diverse backgrounds to review first-year structures independently before delving into new material. A CHANCE TO LEVEL THE LEARNING FIELD Grammaire à réviser?a BRAVO! hallmark and a built-in course equalizer?allows students to self-assess and evaluate their readiness for each chapter's lessons. Grammaire à réviser acts as a diagnostic and/or assessment tool for both the instructor and the learner, checking for mastery of the forms and structures at hand. Those who need the review prior to class have it?and if they need more help on specific grammar points they can to the Workbook/Lab Manual (also available online on the QUIA? platform). If they need even more help, they can access vMentor?, a FREE live online tutoring resource. Students are empowered to come to class on a more level learning field with those students who are better prepared. As a result, more time will be spent in class using the structures functionally as well as practicing new material. No other intermediate French program offers more in-text and study tools to help instructors teach and students learn?effectively bridging the gap between introductory and intermediate course.

Irving Kaplansk This book is based on notes from a course on set theory and metric spaces taught by Edwin Spanier, and also incorporates with his permission numerous exercises from those notes. The volume includes an Appendix that helps bridge the gap between metric and topological spaces, a Selected Bibliography, and an Index.

Janet L. Solberg Larissa Godish Dugas Judith A. Muyskens Linda L. Harlow

Steven M. Kay