ISBN: 0-471-46737-5
Hardcover
608 pages
April 2004
An engineer’s guide to numerical analysis
To properly function in today’s work environment, engineers
require a working familiarity with numerical analysis. This book
provides that necessary background, striking a balance between
analytical rigor and an applied approach focusing on methods
particular to the solving of engineering problems.
An Introduction to Numerical Analysis for Electrical and Computer
Engineers gives electrical and computer engineering students
their first exposure to numerical analysis and serves as a
refresher for professionals as well. Emphasizing the earlier
stages of numerical analysis for engineers with real-life
solutions for computing and engineering applications, the book:
Forms a logical bridge between first courses in matrix/linear
algebra and the more sophisticated methods of signal processing
and control system courses
Includes MATLAB-oriented examples, with a quick introduction to
MATLAB for those who need it
Provides detailed proofs and derivations for many key results
Specifically tailored to the needs of computer and electrical
engineers, this is the resource engineers have long needed in
order to master an area of mathematics critical to their
profession.
ISBN: 0-470-85151-1
Hardcover
214 pages
May 2004
In Geography and GIS, surfaces can be analysed and visualised
through various data structures, and topological data structures
describe surfaces in the form of a relationship between certain
surface-specific features. Drawn from many disciplines with a
strong applied aspect, this is a research-led, interdisciplinary
approach to the creation, analysis and visualisation of surfaces,
focussing on topological data structures.
Topological Data Structures for Surfaces: an introduction for
Geographical Information Science describes the concepts and
applications of these data structures. The book focuses on how
these data structures can be used to analyse and visualise
surface datasets from a range of disciplines such as human
geography, computer graphics, metrology, and physical geography.
Divided into two Parts, Part I defines the topological surface
data structures and explains the various automated methods used
for their generation. Part II demonstrates a number of
applications of surface networks in diverse fields, ranging from
sub-atomic particle collision visualisation to the study of
population density patterns. To ensure that the material is
accessible, each Part is prefaced by an overview of the
techniques and application.
Provides GI scientists and geographers with an accessible
overview of current surface topology research.
Algorithms are presented and explained with practical examples of
their usage.
Features an accompanying website developed by the Editor.
This book is invaluable for researchers and postgraduate students
working in departments of GI Science, Geography and Computer
Science. It also constitutes key reference material for Masters
students working on surface analysis projects as part of a GI
Science or Computer Science programme.
ISBN: 0-470-85055-8
Hardcover
224 pages
September 2004
With the advent of the Web along with the unprecedented amount of
information available in electronic format, conceptual data
analysis is more useful and practical than ever, because this
technology addresses important limitations of the systems that
currently support users in their quest for information. Concept
Data Analysis: Theory & Applications is the first book that
provides a comprehensive treatment of the full range of
algorithms available for conceptual data analysis, spanning
creation, maintenance, display and manipulation of concept
lattices. The accompanying website allows you to gain a greater
understanding of the principles covered in the book through
actively working on the topics discussed.
The three main areas explored are interactive mining of documents
or collections of documents (including Web documents), automatic
text ranking, and rule mining from structured data. The
potentials of conceptual data analysis in the application areas
being considered are further illustrated by two detailed case
studies.
Concept Data Analysis: Theory & Applications is essential for
researchers active in information processing and management and
industry practitioners who are interested in creating a
commercial product for conceptual data analysis or developing
content management applications.
ISBN: 0-471-67908-9
Paperback
309 pages
May 2004
The book that brings order to chaos
Fifteen years ago, Francis Moon’s Chaotic Vibrations
introduced, in practical language, the new ideas of nonlinear
dynamics and chaos. Since then, the field has grown tremendously,
and "chaos" has entered the vocabulary of not only
physicists and mathematicians but also the general public.
Researchers in nonlinear dynamics have broadened their scope to
investigate ideas of complexity in natural and human-made
systems, including networks in the brain, electric energy grids,
and the Internet.
With this paperback reissue of Professor Moon’s classic, we
hope to interest a new generation of readers intrigued by
unpredictability in the laws of physics and its manifestation in
the physical world in the form of chaotic dynamics.
Written for engineers and applied scientists, Chaotic Vibrations
gives specific examples and applications of chaotic dynamics in
the physical world. It also describes how to perform both
computer and physical experiments in chaotic dynamics. Topics
covered include:
Poincare maps
Fractal dimensions
Lyapunov exponents
Experiments in chaos
Chaos in engineering
Similar in its broad scope to James Gleick’s Chaos, only geared
to a more technically curious reader, Chaotic Vibrations features
an extensive guide to the literature, especially as it relates to
more mathematically-oriented works; a glossary of nonlinear
dynamics terms; a list of computer experiments; and details for a
demonstration experiment on chaotic vibrations. This handy
paperback version of Chaotic Vibrations arms engineers and
researchers with the new tools of dynamical systems and prepares
them to make their own contributions to this exciting and rapidly
developing field.
ISBN: 0-470-09043-X
Hardcover
440 pages
September 2004
Statistical techniques that take account of missing data in a
clinical trial, census, or other experiments, observational
studies, and surveys are of increasing importance. The use of
increasingly powerful computers and algorithms has made it
possible to study statistical problems from a Bayesian
perspective. These topics are highly active research areas and
have important applications across a wide range of disciplines.
This book is a collection of articles from leading researchers on
statistical methods relating to missing data analysis, causal
inference, and statistical modeling, including multiple
imputation, propensity scores, instrumental variables, and
Bayesian inference. The book is dedicated to Professor Donald
Rubin, on the occasion of his 60th birthday, in recognition of
his many and wide-ranging contributions to statistics,
particularly to the topic of statistical analysis with missing
data.
Provides an authoritative overview of several important
statistical topics for both research and applications.
Adopts a pragmatic approach to describing a wide range of
intermediate and advanced statistical techniques.
Covers key topics such as multiple imputation, propensity scores,
instrumental variables and Bayesian inference.
Includes a range of applications from the social, health,
biological, and physical sciences.
Features overview chapters for each part of the book.
Edited and authored by highly respected researchers in the area.
Applied Bayesian Modeling and Causal Inference from Incomplete-Data
Perspectives presents an overview with examples of these key
topics suitable for researchers in all areas of statistics. It
adopts a practical approach suitable for applied statisticians
working in social and political sciences, biological and medical
sciences, and physical sciences, as well as graduate students of
statistics and biostatistics.
ISBN: 0-471-20827-2
Hardcover
416 pages
September 2004
This volumes focuses on the theory of statistical inference under
inequality constraints, providing a unified and up-to-date
treatment of the methodology. The scope of applications of the
presented methodology and theory in different fields is clearly
illustrated by using examples from several areas, especially
sociology, econometrics, and biostatistics. The authors also
discuss a broad range of other inequality constrained inference
problems, which do not fit well in the contemplated unified
framework, providing meaningful access to comprehend
methodological resolutions.