Lean Six Sigma Statistics: Calculating Process Efficiencies in Transactional Project (Six SIGMA Operational Methods) Review

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Lean Six Sigma Statistics: Calculating Process Efficiencies in Transactional Project (Six SIGMA Operational Methods) ReviewThis book is a must reference for any Black Belt or Master Black Belt involved in transactional projects. It is full of real world examples and useful advice. This book covers the full breath of the transactional LSS environment from development and implementation of a LSS program ( Recognize and Sustain) to the practical how to's, do's and don'ts of running transactional LSS projects (DMAIC). Buy it, you will not be disappointed.Lean Six Sigma Statistics: Calculating Process Efficiencies in Transactional Project (Six SIGMA Operational Methods) OverviewThe marriage between Lean Manufacturing and Six Sigma has proven to be a powerful tool for cutting waste and improving the organization's operations. This third book in the Six Sigma Operations series picks up where other books on the subject leave off by providing the six sigma practioners with a statistical guide for solving problems they may encounter in implementing and managing a Lean Six Sigma programs. The book draws it examples from all sectors of business ranging from financial to manufacturing providing the reader with a wealth of case studies and as numerous worked out equations which are designed to facilitate the full potential of any Lean Six Sigma project.

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Using R for Introductory Statistics Review

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Using R for Introductory Statistics ReviewThis book doesn't show up under most listings for books about R, but it should. It's a very solid introduction to using R -- including installation, configuration, and some progrmaming -- for basic statistical work. My only complaint is that it wasn't quite comprehensive enough -- not enough examples were given and not enough discussion on important functions and parameters were present. Also, the index is atrocious.
I would recommend it as a good book to get going, but for in depth work you'll be referring to the HTML help a lot.Using R for Introductory Statistics OverviewThe cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption.Using R for Introductory Statistics fills this gap in the literature, making the software accessible to the introductory student. The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models.This text lays the foundation for further study and development in statistics using R. Appendices cover installation, graphical user interfaces, and teaching with R, as well as information on writing functions and producing graphics. This is an ideal text for integrating the study of statistics with a powerful computational tool.

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Statistical Engineering: An Algorithm for Reducing Variation in Manufacturing Processes Review

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Statistical Engineering: An Algorithm for Reducing Variation in Manufacturing Processes ReviewThis is NOT a typical book on statistical tools. It is a strategy book on how to search for cost-effective changes to reduce variation using empirical means (i.e. observation and experiment). The uniqueness of this book:
(1) Summarizes the seven ways to reduce variation so we know the goal of the data gathering and analysis
(2) Present analysis results using graphs instead of P-value.
(3) It integrates Taguchi, Shainin methods and classical statistical approach.
It is a must read for those who are in the business of reducing variation using data, in particular for the Six Sigma Black Belts and Master Black Belts. Don't forget to read the solutions to exercises and supplementary materials to each chapter on the enclosed CD-ROM.Statistical Engineering: An Algorithm for Reducing Variation in Manufacturing Processes OverviewReducing the variation in process outputs is a key part of process improvement. For mass produced components and assemblies, reducing variation can simultaneously reduce overall cost, improve function and increase customer satisfaction with the product.The authors have structured this book around an algorithm for reducing process variation that they call "Statistical Engineering." The algorithm is designed to solve chronic problems on existing high to medium volume manufacturing and assembly processes. The fundamental basis for the algorithm is the belief that we will discover cost effective changes to the process that will reduce variation if we increase our knowledge of how and why a process behaves as it does. A key way to increase process knowledge is to learn empirically, that is, to learn by observation and experimentation.The authors discuss in detail a framework for planning and analyzing empirical investigations, known by its acronym QPDAC (Question, Plan, Data, Analysis, Conclusion). They classify all effective ways to reduce variation into seven approaches. A unique aspect of the algorithm forces early consideration of the feasibility of each of the approaches.Benefits:The CD-ROM included contains case studies, chapter exercises, chapter supplements, and six appendices.

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S Programming Review

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S Programming Review
For those seeking an introduction to programming in S, or seeking information on how to use S for statistical applications, this is NOT a good choice as a "first purchase", nor is it intended to be. Instead, choose these authors' other textbook, Modern Applied Statistics with S-Plus.
This book isn't primarily about using S, using S-PLUS (a commercial version of S), nor using R (an Open Source version of S), but rather, it's about showing how to write programming extensions to the base S functions. In fact, for those seeking such guidance, this is the book's great virtue.
It's written by two world-class authorities on the subject of S programming, persons who are generous in their efforts to help others and who can be contacted through the Internet. However, this particular text assumes the reader is already committed to using S, and hence, contains little 'motivational' material. Yet for the audience to whom it's addressed, it's essential, or at least highly recommended, reading.
The latest commercial version of S (S-PLUS 2000 from Mathsoft, Inc.) has a graphical user interface (GUI). A chapter is included in the latter portion of the book on how to program such interfaces.
Also, a chapter is devoted to extending S with compiled code written in C, Fortran, or C++. Since S is an interpreted language, using compiled code can increase the speed of newly created functions. This is highly technical, however, and not for the neophyte.

Potential software developers of vertical-market applications involving S-PLUS, among others, will benefit from purchasing "S Programming" by Venables & Ripley.S Programming OverviewS is a high-level language for manipulating, analysing and displayingdata. It forms the basis of two highly acclaimed and widely used dataanalysis software systems, the commercial S-PLUS and the OpenSource R. This book provides an in-depth guide to writing software inthe S language under either or both of those systems. It is intendedfor readers who have some acquaintance with the S language and want toknow how to use it more effectively, for example to build re-usabletools for streamlining routine data analysis or to implement newstatistical methods.One of the outstanding strengths of the S language is the ease withwhich it can be extended by users. S is a functional language, andfunctions written by users are first-class objects treated in the sameway as functions provided by the system. S code is eminently readableand so a good way to document precisely what algorithms were used, andas much of the implementations are themselves written in S, they can bestudied as models and to understand their subtleties. The currentimplementations also provide easy ways for S functions to callcompiled code written in C, Fortran and similar languages; this isdocumented here in depth.Increasingly S is being used for statistical or graphical analysiswithin larger software systems or for whole vertical-marketapplications. The interface facilities are most developed onWindows and these are covered with worked examples.The authors have written the widely used Modern Applied Statisticswith S-PLUS, now in its third edition, and several software librariesthat enhance S-PLUS and R; these and the examples used in both booksare available on the Internet.Dr. W.N. Venables is a senior Statistician with the CSIRO/CMISEnvironmetrics Project in Australia, having been at the Department ofStatistics, University of Adelaide for many years previously.Professor B.D. Ripley holds the Chair of Applied Statistics at theUniversity of Oxford, and is the author of four other books on spatialstatistics, simulation, pattern recognition and neural networks. Bothauthors are known and respected throughout the international S and Rcommunities, for their books, workshops, short courses, freelyavailable software and through their extensive contributions to theS-news and R mailing lists.

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Ecological Models and Data in R Review

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Ecological Models and Data in R ReviewThis book, in part, was developed from Dr. Bolker's graduate course in Ecological Models and Data at the University of Florida. This was the best course I took as a graduate student, it transformed the set of quantitative tools I was able to bring to bear on ecological questions. There was so much worthwhile material covered in this class that I took it twice (UF only counted the first time:). Since graduate school I still frequently refer to my notes from the class. With the publication of "Ecological Models and Data in R" even those who didn't have the good fortune of being in Bolker's class can learn approaches for integrating ecological theory and data. Bolker's book covers much of the material from his course and thus is an excellent resource for graduate students and faculty alike.Ecological Models and Data in R Overview
Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics.

Practical, beginner-friendly introduction to modern statistical techniques for ecology using the programming language R
Step-by-step instructions for fitting models to messy, real-world data
Balanced view of different statistical approaches
Wide coverage of techniques--from simple (distribution fitting) to complex (state-space modeling)
Techniques for data manipulation and graphical display
Companion Web site with data and R code for all examples


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Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies Review

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Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies ReviewQuite introductory material, good for beginners (students as well as teachers planning a course) and strong in showing the links between critical discourse analysis and corpus linguistics.Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies OverviewIntroducing Electronic Text Analysis is a practical and much needed introduction to corpora-bodies of linguistic data. Written specifically for students studying this topic for the first time, the book begins with a discussion of the underlying principles of electronic text analysis. It then examines how these corpora enhance our understanding of literary and non-literary works. In the first section the author introduces the concepts of concordance and lexical frequency, concepts whichare then applied to a range of areas of language study. Key areas examined are the use of on-line corpora to complement traditional stylistic analysis, and the ways in which methods such as concordance and frequency counts can reveal a particular ideology within a text. Presenting an accessible and thorough understanding of the underlying principles of electronic text analysis, the book contains abundant illustrative examples and a glossary with definitions of main concepts. Itwill also besupported by a companion website with links to on-line corpora so that students can apply their knowledge to further study. The accompanying website to this book can be found at http://www.routledge.com/textbooks/0415320216

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Handbook of Statistical Analysis and Data Mining Applications Review

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Handbook of Statistical Analysis and Data Mining Applications ReviewThe "Handbook of Statistical Analysis & Data Mining Applications" is the finest book I have seen on the subject. It is not only a beautifully crafted book, with numerous color graphs, chart, tables, and screen shots, but the statistical discussion is both clear and comprehensive.
The text does not use only one statistical data mining application to display examples, but provides a rather thorough training in the use of both SAS-Enterprise Miner and STATISTICA Data Miner. A section on SPSS Clementine is also provided, giving comparisons between the various packages. Also employed are STATISTICA's C&RT, CHAID, MARSpline, and other data mining and graphical analytic tools.
The text does not burden the typical data mining researcher with the internals of how the various tools work. It is therefore not steeped in equations. Some are to be found, of course, but the emphasis is on understanding the concepts involved and on how to apply these concepts to real data - which is provided to the reader in terms of data tutorials. Specialized datasets have been prepared by both authors and outside experts in various areas of inquiry ranging from entertainment, financial, engineering, clinical psychology, dentistry, demographics, medical informatics, meteorology, astronomy, and more. Each tutorial is associated with data stored on either the associated CD that comes with the book, or which can be downloaded from a companion web site. Worked out examples of how to use data mining techniques on such data is provided to help the reader gain a solid feel for the data mining enterprise. The final third of the book is devoted to a partial selection of the available tutorials. The two earlier chapters demonstrate how to use data mining software for the analysis of data.
I highly recommend this work to anyone having an interest in data mining. I might also add that the Amazon price of $72.37 is truly excellent for an 864 page academic text, having full color tables and screen shots on some one-third of the pages, plus a CD. A bargain indeed.
Handbook of Statistical Analysis and Data Mining Applications OverviewThe Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.

Written "By Practitioners for Practitioners"
Non-technical explanations build understanding without jargon and equations
Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software
Practical advice from successful real-world implementations
Includes extensive case studies, examples, MS PowerPoint slides and datasets
CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book


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R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) Review

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R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) ReviewThis is a strange little book in that it seems somewhat directed toward statisticians who want to develop R packages. The OOP section takes up 50 pages and discusses "S3 and S4" implementations of OOP in R in great detail, all of which is not doubt important for those few dozen accomplished statisticians who wish to write packages. However, by the time you are ready to actually write an R function that other people will use I can't imagine you wouldn't already be familiar with some of the basic commands discussed elsewhere in this book. So I am wondering who the intended audience is.
I think the majority of R users (biologists and programmers) want to run through some common statistical routines in a procedural fashion and produce reports that perform some analysis and show some graphs. The difficulty with R is learning how to massage data into a form that an existing statistical function will accept. That will invariably involve helper R-specific helper functions that do not exist in programming languages (e.g. unsplit) or that require a precise understanding of input (e.g. xtabs), and statistical routines that almost never return meaningful errors (glm). Manipulating data structures in R is not particularly intuitive (e.g. as.numeric(levels(f))[f]), so tons of examples are a must. However this book simply does not include enough R code - probably fewer than 250 lines.
In some instances commands are discussed at length in the space it would take to simply show the command. For example, a beginner would want to know how to save a data frame. Instead of providing a useful example like:
save(myDataFrame,file="myDataFrame.frame.RData",compress=TRUE)
there is a bizarre paragraph called "Working with R's binary format", in which save and load are discussed in theory as if they are planned for a distant release.
There is no chapter on using Sweave to develop pdf reports despite the book being actually written in Sweave. The author is more focused on "vignettes" which appear to be for documentation akin to POD files.
This book does include excellent sections on string manipulation, connecting to databases, and C integration. I learned some things about some neat Bioconductor functions available but a dedicated chapter would be nice.
At no point do you ever sense the author does not know what he is talking about - he just doesn't know who he is talking to. I hope in the future "R Programming For Bioinformatics" is split this into two more comprehensive books: "Developing R Packages" and "R for Biologists"R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) OverviewDue to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems.Drawing on the author's first-hand experiences as an expert in R, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code.With numerous examples and exercises, this practical guide focuses on developing R programming skills in order to tackle problems encountered in bioinformatics and computational biology.

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Statistical Machine Translation Review

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Statistical Machine Translation ReviewPhilipp Koehn is a superb lecturer and teacher in the area of statistical machine translation (SMT). I have being living off his lecture notes from the ACL, LSA summer session and Edinburgh for years and eagerly waiting for this book to tie everything together.
Koehn has the ability to take complex statistical concepts and make them comprehensible. And he has an encyclopedic knowledge of the state-of-the-art in SMT. His bibliography alone is worth the price of this book.
This book will be the gold standard in SMT for years to come. I would highly recommend to students and professionals in the field.Statistical Machine Translation OverviewThis introductory text to statistical machine translation (SMT) provides all of the theories and methods needed to build a statistical machine translator, such as Google Language Tools and Babelfish.In general, statistical techniques allow automatic translation systems to be built quickly for any language-pair using only translated texts and generic software. With increasing globalization, statistical machine translation will be central to communication and commerce. Based on courses and tutorials, and classroom-tested globally, it is ideal for instruction or self-study, for advanced undergraduates and graduate students in computer science and/or computational linguistics, and researchers in natural language processing. The companion website provides open-source corpora and tool-kits.

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Modeling with Data: Tools and Techniques for Scientific Computing Review

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Modeling with Data: Tools and Techniques for Scientific Computing ReviewKlemens teaches how to tame and understand a dataset the way it should be done: in C. Invest some time in this excellent book full of gentle humor and respect for the reader's intelligence. The payoffs will be immense. The best resource (a full set of programs used in the book) accompanying this book is available for FREE(!) on Klemens's website!Modeling with Data: Tools and Techniques for Scientific Computing Overview
Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.


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Spatial Conservation Prioritization: Quantitative Methods and Computational Tools Review

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Spatial Conservation Prioritization: Quantitative Methods and Computational Tools ReviewThis book provides a well-written and accessible introduction to new methods for quantitative conservation planning that have been developed over the past decade. Beyond an audience of planners who actually apply these methods, it is also suitable for conservation biologists who are looking for a general introduction to the concepts underlying this field. It would serve as a good textbook for a seminar in quantitative conservation planning, as the chapters generally combines a high level of detail with a style accessible to graduate and advanced undergraduate students.Spatial Conservation Prioritization: Quantitative Methods and Computational Tools OverviewHabitat loss and deterioration, climate change, and economic pressures for resource extraction have all led to a global loss of biodiversity. The limited resources available for conservation need to be used both effectively and efficiently in order to minimise further losses. Spatial conservation prioritization addresses the question of how we should allocate conservation effort and funds in space and time. While the benefits of quantitative conservation prioritization methods have been widely promoted, adoption of these methods in "real-world" planning and implementation is still in its infancy, partly due to the difficulty of identifying which methods and tools (if any) are suited to specific planning problems. Spatial Conservation Prioritization brings together a team of leading scientists to introduce the conceptual and methodological aspects of how to undertake spatial conservation planning in a quantitative manner. It provides the reader with information on when, why, and how to use which statistical and computational methods for conservation prioritization. Important topics underlying spatial prioritization including metapopulation modelling, population viability analysis modelling, species distribution modelling, and uncertainty analysis are discussed, as well as operational definitions and methods. The book includes chapters on the most widely used and latest software, and concludes with an insight into the future of the field.

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Singular Spectrum Analysis: A New Tool in Time Series Analysis (Language of Science) Review

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Singular Spectrum Analysis: A New Tool in Time Series Analysis (Language of Science) ReviewThis text is divided into three main parts: 1) Mathematical notes, 2) Theory and Methods, 3) Applications. As a reader interested in time series analysis but unfamiliar with singular spectrum analysis (SSA) per se, I was not really sure what SSA was exactly, or what it is suppose to accomplish, until nearly halfway into the book. Very early on, I found no one single sentence, paragraph, definition, or description that clearly defined SSA beyond a "technique based on spectral decomposition."
Although the text is clearly written and well organized, the authors' target audience is not well established. For example, Section I starts with an rather remedial primer on linear matrix algebra, yet Section II already seems to assume the reader has a fairly mature knowledge of statistical inference, hypothesis testing, and principal component analysis (which might be gained from courses on statistical analysis of variance, or ANOVA). The authors also make frequent references to other time series analysis techniques, such as ARMA modeling and Fourier analysis in Section III. Familiarity with ANOVA and other kinds of time series analyses will certainly benefit the reader.
One gets the impression that the manuscript has been enhanced to fill a textbook-sized volume (and would explain why the book dedicates an entire chapter to numerical examples illustrating such things as how to multiply two matrices). The font size is strikingly large for a 6" x 9" textbook, and the layout is noticeably fragmented with lots of section subtitles and large amounts of white space around these section titles. Specifically, each page accommodates no more than 34 lines of 4 1/4" wide text, which I compared with several textbooks this size which usually accommodated 40-45 lines of 4 1/2" wide text (I suspect the 153 page content might have been fitted into about 100 pages just by changing the book layout). Given the quite basic subject matter of the first few chapters, the remainder of the book is but a fairly short survey on the main subject of SSA. The advantage is that its contents can be covered relatively quickly.
The rarity of introductory texts specifically dedicated to this niche subject matter, and the uncomplicated presentation the authors have chosen, make this book a worthwhile, albeit expensive, introduction to the topic. This book has nice presentation qualities, and had it been issued as a thin, inexpensive paperback, this would more easily be a 4 or 5 star book. Similarly, this is a 4 star book for the prospective reader analyzing data related to the atmospheric sciences or climatology (as much of the authors' background materials emphasize these areas). An expanded second edition would be welcomed.Singular Spectrum Analysis: A New Tool in Time Series Analysis (Language of Science) OverviewThis original new text provides an easily accessibleintroduction to this important new topic in time series analysis. Theauthorsemphasize examples over theoretical explanations and the needforproper and careful statistical tests in the context of dataexploration. The book's focus is on the application of the method insignal detection, filtering, and prediction. Instructors and studentswill appreciate the step-by-step presentation of underlying ideas.

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From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) Review

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From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) ReviewIt is easy to read with lots of examples. Gives planty of ideas if you are interested in corpus study.From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) OverviewFrom Corpus to Classroom summarises and makes accessible recent work in corpus research, focusing particularly on spoken data. It is based on analysis of corpora such as CANCODE and Cambridge International Corpus, and written with particular reference to the development of corpus-informed pedagogy.The book explains how corpora can be designed and used, and focuses on what they tell us about language teaching. It examines the relevance of corpora to materials writers, course designers and language teachers and considers the needs of the learner in relation to authentic data. It shows how the answers to key questions such as 'Is there a basic, everyday vocabulary for English?', 'How should idioms be taught?' and 'What are the most common spoken language chunks?' are best explored by means of a clearer understanding of the workings of language in context.

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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) Review

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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) ReviewThis is a good book to learn about Estimation of Distribution Algorithms (EDAs or also called DEAs or Iterated DEAs). These algorithms are similar to evolutionary algorithms, but do not use the crossover or mutation operators of evolutionary search. EDAs instead create a probabilistic model of good solutions and use the model to generate new search points. It's a nifty idea and it works.
Most of the chapters of this edited collection were authored or coauthored by the editors. So, algorithms developed by other people do not get a lot of attention. However, the editors (or is it the authors) manage to include chapters on combinatorial, continuous, and discrete optimization.
There is a section on machine learning applications that is OK, but the last chapter on training neural nets with EDAs is very weak (look ma I used this and it worked...). Except for this chapter, the rest of the chapters in this section use careful experiments and statistics to make their points.
Making the source code available would have improved things and would make it easier for people to try these algorithms.Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) OverviewEstimation of Distribution Algorithms: A New Tool forEvolutionary Computation is devoted to a new paradigm forevolutionary computation, named estimation of distribution algorithms(EDAs). This new class of algorithms generalizes genetic algorithms byreplacing the crossover and mutation operators with learning andsampling from the probability distribution of the best individuals ofthe population at each iteration of the algorithm. Working in such away, the relationships between the variables involved in the problemdomain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of thetechniques and applications of this new tool for performingevolutionary computation. Estimation of Distribution Algorithms: ANew Tool for Evolutionary Computation is clearly divided intothree parts. Part I is dedicated to the foundations of EDAs. In thispart, after introducing some probabilistic graphical models -Bayesian and Gaussian networks - a review of existing EDAapproaches is presented, as well as some new methods based on moreflexible probabilistic graphical models. A mathematical modeling ofdiscrete EDAs is also presented. Part II covers several applicationsof EDAs in some classical optimization problems: the travellingsalesman problem, the job scheduling problem, and the knapsackproblem. EDAs are also applied to the optimization of some well-knowncombinatorial and continuous functions. Part III presents theapplication of EDAs to solve some problems that arise in the machinelearning field: feature subset selection, feature weighting inK-NN classifiers, rule induction, partial abductive inferencein Bayesian networks, partitional clustering, and the search foroptimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool forEvolutionary Computation is a useful and interesting tool forresearchers working in the field of evolutionary computation and forengineers who face real-world optimization problems. This book mayalso be used by graduate students and researchers in computer science.`... I urge those who are interested in EDAs to study thiswell-crafted book today.' David E. Goldberg, University ofIllinois Champaign-Urbana.

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