An Introduction to Data Structures and Algorithms (Progress in Theoretical Computer Science) Review

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(More customer reviews)If you have cut your teeth on Donald Knuth's classic three volumes, "The Art of Computer Programming", and you want more detail, at a similar level of complexity, then consider Storer's book.
It delves into lists, recursion, trees, graphs, heaps and sets. Like Knuth, Storer thoughtfully supplies an extensive list of questions at the end of each chapter that will greatly deepen your appreciation of the field if you tackle them. Ok, he doesn't give answers, but think of that as greater incentive on your part to solve them. There are almost 400 questions in the book.
The teaching style is similar to Knuth, in that it has all the rigour needed by an algorithm designer like yourself, without drowning you in epsilon-delta ultra rigour like a pure maths text.
Note that the only code fragments are in pseudocode. This should not be a problem for you. I am assuming you are experienced enough that what you need is understanding of an algorithm, and that manually converting it to code is straightforward and a purely secondary issue.
The take home message is that this is excellent for anyone doing serious programming.

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Data structures and algorithms are presented at the college level in a highly accessible format that presents material with one-page displays in a way that will appeal to both teachers and students. The thirteen chapters cover: Models of Computation, Lists, Induction and Recursion, Trees, Algorithm Design, Hashing, Heaps, Balanced Trees, Sets Over a Small Universe, Graphs, Strings, Discrete Fourier Transform, Parallel Computation. Key features: Complicated concepts are expressed clearly in a single page with minimal notation and without the "clutter" of the syntax of a particular programming language; algorithms are presented with self-explanatory "pseudo-code." * Chapters 1-4 focus on elementary concepts, the exposition unfolding at a slower pace. Sample exercises with solutions are provided. Sections that may be skipped for an introductory course are starred. Requires only some basic mathematics background and some computer programming experience. * Chapters 5-13 progress at a faster pace. The material is suitable for undergraduates or first-year graduates who need only review Chapters 1 -4. * This book may be used for a one-semester introductory course (based on Chapters 1-4 and portions of the chapters on algorithm design, hashing, and graph algorithms) and for a one-semester advanced course that starts at Chapter 5. A year-long course may be based on the entire book. * Sorting, often perceived as rather technical, is not treated as a separate chapter, but is used in many examples (including bubble sort, merge sort, tree sort, heap sort, quick sort, and several parallel algorithms). Also, lower bounds on sorting by comparisons are included with the presentation of heaps in the context of lower bounds for comparison-based structures. * Chapter 13 on parallel models of computation is something of a mini-book itself, and a good way to end a course. Although it is not clear what parallel

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An Introduction to Programming with IDL: Interactive Data Language Review

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(More customer reviews)Nothing is more intimidating to a new user of IDL than to sit down at a computer with an empty command line prompt and a stack of manuals on their desk and be told to write an IDL program. Where to begin!? And the experience is especially frightening to a new user with little or no programming experience in any language, let alone IDL.
Ken Bowman has written an IDL book specifically for this user. It is intended as an introductory computer programming course for the research user with little or no training in any computer language, and it evolved from notes Ken uses in his own undergraduate IDL programming courses. It is meant to get the new IDL user analyzing and plotting data as soon as possible.
It is a narrow path he treads, because it is just as easy to offer too much detail as it is to offer too little information to the beginning user. Ken, for the most part, gets it exactly right in covering a broad selection of topics. I quibble with just two chapters. He sweeps aside the complexity of PostScript output by offering the new user two utility programs he fails to explain in the text, and his theoretical explanation of the FFT function left me gasping for breath and lamenting I hadn't paid closer attention in those long-ago math classes.
This is a book that will get you started, but probably won't answer all your questions when you turn your attention to more difficult research problems. Ken doesn't pretend it is anything other than what it is, however, and provides generous and helpful suggestions for where you can find additional information as you become ready for it. Readers already familiar with another programming language will appreciate this introduction to IDL, but might become frustrated with the slower pace and lack of specific detail on many topics.
The book has an associated web page, where you can find, among other things, the source code for all the programs mentioned in the book. Pay particular attention to the Errata section, especially if you are interested in structures in IDL. A printer glitch removed all the curly brackets from Ken's IDL code in the structure chapter and none of the examples will work as written in the book. A software problem, no doubt. (The example programs for the chapter are correct.) It serves as a reminder to me of how complex a topic software programming can be. This friendly book will be a welcome introduction to the subject for many a potential IDL programmer.

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In today's information age, scientists and engineers must quickly and efficiently analyze extremely large sets of data. One of the best tools to accomplish this is Interactive Data Language (IDLÂ), a programming and visualization environment that facilitates numerical modeling, data analysis, and image processing.IDL's high-level language and powerful graphics capabilities allow users to write more flexible programs much faster than is possible with other programming languages.An Introduction to Programming with IDL enables students new to programming, as well as those with experience in other programming languages, to rapidly harness IDL's capabilities: fast, interactive performance; array syntax; dynamic data typing; and built-in graphics. Each concept is illustrated with sample code, including many complete short programs. ÂMargin notes throughout the text quickly point readers to the relevant sections of IDL manualsÂEnd-of-chapter summaries and exercises help reinforce learningÂStudents who purchase the book are eligible for a substantial discount on a student version of the IDL software

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Data Structures and Algorithms in Java (2nd Edition) Review

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(More customer reviews)This is an excellent book on data structures and algorithms and would be a great addition to a student's text book. Fortunately Lafore does not dwell on the algorithm analysis as many data structures books do. This is a plus for me, as most texts on the subject get the reader bogged down in the analysis portion of the subject matter. Note: algorithm analysis is a very important subject I just don't believe it should be taught in parallel with data structures to the extent it is. Its easier for me and many others to first learn how to implement data structures and get a feel for their performance then move on to in-depth analysis.
In this book you'll learn the more important data structures without the heavy mathematics many algorithm and data structure books torture readers with. The book is written in very accessible language and the applets really help the inexperienced see the algorithms in action.
As I mentioned this book does not cover algorithm analysis in detail. A step up from this book would be one of Sedgewick's books which provides more detail on the analysis front and some really 'tight' implementations. A good book that focuses on Algorithm Analysis is Intro to Algorithms by Cormen. You better have your math skills up to snuff for the Cormen book however.

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Data Structures and Algorithms in Java, Second Edition is designed to be easy to read and understand although the topic itself is complicated. Algorithms are the procedures that software programs use to manipulate data structures. Besides clear and simple example programs, the author includes a workshop as a small demonstration program executable on a Web browser. The programs demonstrate in graphical form what data structures look like and how they operate. In the second edition, the program is rewritten to improve operation and clarify the algorithms, the example programs are revised to work with the latest version of the Java JDK, and questions and exercises will be added at the end of each chapter making the book evenmore useful.Educational SupplementSuggested solutions to the programming projects found at the end of each chapter are made available to instructors at recognized educational institutions. This educational supplement can be found at www.prenhall.com, in the Instructor Resource Center.

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Introduction to Algorithms: A Creative Approach Review

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(More customer reviews)This book is much more than a catalog of algorithms (e.g., CLR): its purpose is to train your intuition to recognize mathematical structure in abstract problems. What does it matter if you know Dijkstra's algorithm? It's much more valuable to have good intuitions and a inductive reasoning tool chest with which to smash apart all of the variations of the shortest path problem (for example.)
The reviewers who wrote that the book "assumes you are a math wiz" and that it provides "little or no guidance for solving an arbitrary problem of the same type" didn't get it. This book is trying very hard to make you into a wiz by forcing you to really interact with mathematics, rather than working through a set of nearly identical problems (--what passes for "education" in North America.)
I was just going to leave my review at that, but since the reviews that people find "helpful" are so way off base, I think I should throw in a relevant story.
When my friend was in grade 11, he showed up to the Canadian Computing Competition finals, placing 14th. The guy who won told him, "if you want to win, read this book." Two years later, he won the CCC against 2000 other students. This book is the best introduction you can give a budding mathematician.
Sure, you can cough up what you've memorized from CLR during your university algorithms course. But, do you want to learn to invent algorithms yourself?
Math is not something handed down generations in big, dry textbooks. Like all knowledge, math is organically discovered Truth, and you have learn to discover it for yourself.

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This book emphasizes the creative aspects of algorithm design by examining steps used in the process of algorithms development. The heart of this creative process lies in an analogy between proving mathematical theorems by induction and designing combinatorial algorithms. The book contains hundreds of problems and examples. It is designed to enhance the reader's problem-solving abilities and understanding of the principles behind algorithm design.

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Purely Functional Data Structures Review

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(More customer reviews)Okasaki's slim volume is one of the best expositions on implementing data structures & algorithms in a functional language. After taking an introductory course on functional programming, this would be the book which tells you where to go next.
This book doesn't just present a rehash/rewrite of imperative data structures, only written in a functional language. Instead, Okasaki makes sure to emphasize benefits which only functional programming can bring to the table. For example, many functional data structures can compactly represent not just their current state, but all of their past states as well--a feature called "Persistence". Also, functional newbie programmers might be wondering why lazy vs. strict programming is a big deal, and Okasaki shows clearly where data structures can benefit from either being lazy or being strict.
For the advanced reader, Okasaki also presents several powerful techniques for analyzing the runtime of algorithms, including the so-called "Banker's Method" and the "Physicist's Method" for analyzing amortized algorithms.
I hope that Okasaki comes out with a 2nd edition of this book; there is one missing piece in particular which I really wish he would have included: Although he presents an EXTREMELY lucid description of how to implement Red-Black trees in a functional language, he only presented algorithms for insertion and querying. Of course, deletion from a red-black tree is the hardest part, left here, I suppose, as an exercise to the student. If you want to supply this missing piece yourself, check out a paper by Stefan Kars, "Red-black trees with types", J. Functional Programming 11(4):425-432, July, 2001. It presents deletion routines, but you'll still want to read Okasaki's book first, for unless you're very much smarter than me you won't be able to understand Kars' paper until you read Okasaki's exposition of red black trees.
Finally, this book is not just useful for programmers in functional languages; logic programmers, using prolog or a varient, will also find this book very helpful, because most of the techniques (all of the techniques, really, with the exception perhaps of the lazy programming stuff) can be directly applied in a prolog programming setting as well.
After reading this book and implementing some of the data structures for yourself, you'll be amazed at how fast algorithms can run, even when written in a functional language!

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Most books on data structures assume an imperative language such as C or C++.However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme.This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures.The author includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages.All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.

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Designing Concurrent, Distributed, and Real-Time Applications with UML Review

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(More customer reviews)This book provides a practical method to apply the Unified Modeling Language (UML) to design concurrent and distributed software for large systems. While many books have been published to explain the details of the UML notation, this book provides a method, COMET, to approach software design through application of a practical subset of the UML notation. The book contains numerous, extensive case studies and provides pragmatic, useful guidelines to identify distributed subsystems and concurrent tasks from a UML analysis model. The approach described represents a unique and valualbe contribution by the author of this text. Finally, because this book is intended mainly as a text for software design courses, I consulted with a few students who have used this book in a graduate-level software engineering course. All of the students were favorably impressed with the content, clarity, practicality, and detail contained in the book.
Prior to the publication of this text, a software design course based on UML could only be taught using a UML text together with a separate software design text. In this text book, Dr. Gomaa has integrated material from UML and software design in such a form that a software design course can now be taught with this text alone.

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In this book, renowned real-time software expert Hassan Gomaa adapts UML to the unique needs of the concurrent, distributed, and real-time applications -- helping developers leverage the powerful flexibility, reliability, and time-to-market benefits associated with UML. Gomaa starts by reviewing the key issues and concepts associated with analysis and design of distributed and real-time applications -- focusing not only on standard object-oriented concepts such as information hiding, classes, and inheritance, but also specialized issues such as finite state machines, concurrent tasks, and real-time scheduling. Next, he introduces the COMET (Concurrent Object Modeling and Architectural Design) Method, a UML-based object-oriented analysis and design method specifically created for concurrent, distributed, and real-time applications. The book presents detailed structuring criteria that assist the designer at every stage of the analysis and design process, and offers exceptional insight into dynamic modeling, concurrency, distributed application design, and performance analysis of real-time designs.Gomaa concludes with several detailed case studies covering a broad range of applications, including systems for banking, e-Commerce, cruise control, factory automation, and more.

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Handbook of Mathematical Models in Computer Vision Review

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(More customer reviews)When attending a general computer vision conference like xCCV, did you ever feel lost at certain sessions? Well, don't always blame the presenters! The field covered by Computer Vision has become so broad that it is almost impossible to understand what is going on and to keep track of the latest developments. To (partially) overcome this problem, the editors of the Handbook of Mathematical Models in Computer Vision have done a great job.
One can become a bit skeptical reading such a title. How complete can such a handbook be? However, going through the 33 chapters, indeed a wide breadth is treated. The focus of the book is on mathematical methods that both model and reproduce human visual abilities. This is the field of biological vision in which the editors have a strong background.
The editors chose three distinct categories of mathematical models, namely variational techniques (those attending Prof. Faugeras' talk at ICPR 2006 may remember his statement that they give the fundamental equations in computer vision!), statistical methods, and combinatorial approaches. The chapters are grouped in six sections that circle around these three categories. Although going through the book chapters by mentioning keyword may yield a rather boring list, it shows the wide variety of topics that are being dealt with.
The book starts with a section on low-level vision: Image Reconstruction. Here one can find information on diffusion filters and wavelets, total variation methods, and PDE based inpainting.
The second section is concerned with Boundary Extraction, Segmentation and Grouping. Here subjects like levelings, graph cuts, minimal paths and fast marching methods, deformable models, variational segmentation with shape priors, curve propagation, level set methods, and a stochastic model of geometric snakes are discussed.
Section three switches to high level vision. It deals with Shape Modeling & Registration, divided into topics concerning invariant processing and occlusion resistant recognition, image-based inferences, point matching and uncertainty-driven, point-based image registration.
In the fourth section, Motion Analysis, Optical Flow & Tracking, the concept of time is added and one encounters the topics of optical flow estimation, image warping, alignment and stitching, visual tracking, image and video segmentation, human motion capture, and dynamic textures.
Section five deals with 3D from Images, Projective Geometry & Stereo Reconstruction, treated by boundary detection, stereo, texture and color, shape from shading, calibration, motion and shape recovery, multi-view reconstruction, binocular stereo with occlusions, and modeling non-rigid dynamic scenes.
The last section may seem a bit odd: Applications: Medical Image Analysis. However, this is one of the most prominent areas in computer vision. Although here certain vision aspects do not occur, compared to natural images (just think of the influence of the sun), for many tasks the performance of the mathematical methods can be evaluated since a ground truth is often available - provided by humans whom the models are supposed to mimic. In this section, applications of interactive graph-based segmentation methods, 3D active shape and appearance models, characterization of diffusion anisotropy, segmentation, variational approaches, and statistical methods of registration are given.
The danger of publishing an edited volume is the difference in style and treatment of the topics among the various contributions. This is not the case here. Each chapter gives a general introduction to the topic, introduces the mathematical model, discusses the underlying ideas globally, and shows some results. For the full details the readers are referred to the extensive bibliography with 929 entries.
This book is a must-have for those interested in the full breadth of research done in the biological & computer vision community. As a bonus, the chapters can also be used in a seminar-based, advanced undergraduate course in mathematical based computer vision.


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This comprehensive volume is an essential reference tool for professional and academic researchers in the filed of computer vision, image processing, and applied mathematics. Continuing rapid advances in image processing have been enhanced by the theoretical efforts of mathematicians and engineers. This marriage of mathematics and computer vision - computational vision - has resulted in a discrete approach to image processing that is more reliable when leveraging in practical tasks. This comprehensive volume provides a detailed discourse on the mathematical models used in computational vision from leading educators and active research experts in this field. Topical areas include: image reconstruction, segmentation and object extraction, shape modeling and registration, motion analysis and tracking, and 3D from images, geometry and reconstruction. The book also includes a study of applications in medical image analysis.Handbook of Mathematical Models in Computer Vision provides a graduate-level treatment of this subject as well as serving as a complete reference work for professionals.

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Visualizing Data: Exploring and Explaining Data with the Processing Environment Review

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(More customer reviews)This book allowed me to quickly create some simple applications using the processing API. So, in that respect, the book was successful. However, the book falls short in three respects.
1) One would expect a book with the title "Visualizing Data" to be crammed with pictures showing many different data visualizations. However, this book has relatively few. Every colleague of mine who passed by my desk and picked up the book had the exact same reaction.
2) The processing language is touted as a means for people unfamiliar with programming to get up to speed with visualization. However, I would be very surprised if anyone with little programming experience would get much out of this book.
3) Don't expect to use this book as a reference for the processing language. It is basically just a collection of half explained examples. Consider for example the function smooth(). This function appears in almost every example but forget about trying to find an explanation of what the function does in the book.
The book is probably worth buying to get up to speed quickly but plan on spending a significant amount of time sifting through the processing.org website and other online resources before being able to get anything non-trivial done. And if you don't already know Java then don't expect to accomplish anything even modestly complex without a lot of outside help.


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Enormous quantities of data go unused or underused today, simply because people can't visualize the quantities and relationships in it. Using a downloadable programming environment developed by the author, Visualizing Data demonstrates methods for representing data accurately on the Web and elsewhere, complete with user interaction, animation, and more. How do the 3.1 billion A, C, G and T letters of the human genome compare to those of a chimp or a mouse? What do the paths that millions of visitors take through a web site look like? With Visualizing Data, you learn how to answer complex questions like these with thoroughly interactive displays. We're not talking about cookie-cutter charts and graphs. This book teaches you how to design entire interfaces around large, complex data sets with the help of a powerful new design and prototyping tool called "Processing". Used by many researchers and companies to convey specific data in a clear and understandable manner, the Processing beta is available free. With this tool and Visualizing Data as a guide, you'll learn basic visualization principles, how to choose the right kind of display for your purposes, and how to provide interactive features that will bring users to your site over and over. This book teaches you:

The seven stages of visualizing data -- acquire, parse, filter, mine, represent, refine, and interact
How all data problems begin with a question and end with a narrative construct that provides a clear answer without extraneous details
Several example projects with the code to make them work
Positive and negative points of each representation discussed. The focus is on customization so that each one best suits what you want to convey about your data set
The book does not provide ready-made "visualizations" that can be plugged into any data set. Instead, with chapters divided by types of data rather than types of display, you'll learn how each visualization conveys the unique properties of the data it represents -- why the data was collected, what's interesting about it, and what stories it can tell. Visualizing Data teaches you how to answer questions, not simply display information.

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Metaheuristics: From Design to Implementation (Wiley Series on Parallel and Distributed Computing) Review

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(More customer reviews)The book is a good and detailed description of what metaheuristics involves. This is applied to solving hard computational problems. There are summaries of many methods developed over the last 50 years. The simplex method. Metropolis Monte Carlo. Simulated annealing. Genetic algorithms. And others. There is deliberately not enough information about most of these for you to use them given only the book as a starting point. Space considerations.
But mostly the book explains at a higher level, how methods can be understood. Some are for exploiting; ie. intensively looking in a given region of the objective space around a starting point. Simulated annealing is a good example of such a method.
Other methods are for exploring. A broader search in the objective or solution space. Genetic algorithms, with their mutations and crossover recombinations are very strong here, using ideas borrowed from biological evolution.
More importantly, the book shows how many hard problems have to be tackled by a combination of exploring and exploiting. The combining of algorithms is what gives metaheuristics its name.
One caveat is that even at a summary level, the description of tabu search was a bit unclear, compared to the excellent synopses of simulated annealing and genetic algorithms.

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A unified view of metaheuristics
This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code.
Throughout the book, the key search components of metaheuristics are considered as a toolbox for:

Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems

Designing efficient metaheuristics for multi-objective optimization problems

Designing hybrid, parallel, and distributed metaheuristics

Implementing metaheuristics on sequential and parallel machines

Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) Review

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(More customer reviews)Stanford professor, Daphne Koller, and her co-author, Professor Nir Friedman, employed graphical models to motivate thoroughgoing explorations of representation, inference and learning in both Bayesian networks and Markov networks. They do their own bidding at the book's web page, [...], by giving readers a panoramic view of the book in an introductory chapter and a Table of Contents. On the same page, there is a link to an extensive Errata file which lists all the known errors and corrections made in subsequent printings of the book - all the corrections had been incorporated into the copy I have. The authors painstakingly provided necessary background materials from both probability theory and graph theory in the second chapter. Furthermore, in an Appendix, more tutorials are offered on information theory, algorithms and combinatorial optimization. This book is an authoritative extension of Professor Judea Pearl's seminal work on developing the Bayesian Networks framework for causal reasoning and decision making under uncertainty. Before this book was published, I sent an e-mail to Professor Koller requesting some clarification of her paper on object-oriented Bayesian networks; she was most generous in writing an elaborate reply with deliberate speed.

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A general framework for constructing and using probabilistic models ofcomplex systems that would enable a computer to use available information for makingdecisions.

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Numerical Geometry of Non-Rigid Shapes (Monographs in Computer Science) Review

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(More customer reviews)Numerical geometry of non-rigid shapes is the first attempt to present a focused and broad study of topics in non-rigid shape analysis. The book presents theoretical foundations, methods, algorithms and applications involving non-rigid shapes in different fields including computer vision, pattern recognition, and computer graphics. A special focus is made on practical value of the book - it is accompanied with code examples and references to commercial and public-domain software. Recommended as a textbook for computer vision and pattern recognition courses, reference for students and experts in the field.

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Deformable objects are ubiquitous in the world surrounding us, on all levels from micro to macro. The need to study such shapes and model their behavior arises in a wide spectrum of applications, ranging from medicine to security. In recent years, non-rigid shapes have attracted growing interest, which has led to rapid development of the field, where state-of-the-art results from very different sciences - theoretical and numerical geometry, optimization, linear algebra, graph theory, machine learning and computer graphics, to mention several - are applied to find solutions.This book gives an overview of the current state of science in analysis and synthesis of non-rigid shapes. Everyday examples are used to explain concepts and to illustrate different techniques. The presentation unfolds systematically and numerous figures enrich the engaging exposition. Practice problems follow at the end of each chapter, with detailed solutions to selected problems in the appendix. A gallery of colored images enhances the text.This book will be of interest to graduate students, researchers and professionals in different fields of mathematics, computer science and engineering. It may be used for courses in computer vision, numerical geometry and geometric modeling and computer graphics or for self-study.

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Robotics: Modelling, Planning and Control (Advanced Textbooks in Control and Signal Processing) Review

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(More customer reviews)This book has everything a new grad student or aspiring roboticist would want. It covers the mechanics, the controls and even some vision for robotic manipulators. Very nice resource.

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Based on the successful Modelling and Control of Robot Manipulators by Sciavicco and Siciliano (Springer, 2000), Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control. It has been expanded to include coverage of mobile robots, visual control and motion planning. A variety of problems is raised throughout, and the proper tools to find engineering-oriented solutions are introduced and explained.The text includes coverage of fundamental topics like kinematics, and trajectory planning and related technological aspects including actuators and sensors.To impart practical skill, examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, end-of-chapter exercises are proposed, and the book is accompanied by an electronic solutions manual containing the MATLAB code for computer problems; this is available free of charge to those adopting this volume as a textbook for courses.

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Simulation, Third Edition (Statistical Modeling and Decision Science) Review

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(More customer reviews)According to the books I have consulted by S. M. Ross, I consider him to
be a tremendous author, with this book being my absolute favourite. It
is inspirational, clear, creative and (most of all) fun to read.
Starting off from the very basics regarding probability, random numbers
and generation of random variables, Professor Ross steadily guides the
reader through, for instance, simulation with respect to some queueing
systems, validation techniques (e.g. goodness-of-fit tests;
Kolmogorov-Smirnov tests), the standard MCMC-methods and various variance
reduction techniques. The latter chapter is the one that I found most
enlightening including, for instance, a certainly rewarding section on
importance sampling (weighted simulation). Throughout, a lot of
well-chosen examples enhance students/readers understanding and interest
of the subject.
One might note that this is by no means the most advanced or complete book
on Monte Carlo simulation out there, see e.g.
Monte Carlo Statistical Methods (Springer Texts in Statistics)
or
Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability),
rather it evolves at an intermediate level covering a few more advanced
concepts as, for example, coupling from the past. But, having this in
mind, this is the book to get. Pure concentrated fun (xiii+274 pages).
Final note: In 2006 the fourth edition was launched. [Simulation, Fourth Edition]


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Sheldon Ross' Simulation, Third Edition introduces aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes. This new edition provides a comprehensive, in-depth, and current guide for constructing probability models and simulations for a variety of purposes. It features new information, including the presentation of the Insurance Risk Model, generating a Random Vector, and evaluating an Exotic Option. Also new is coverage of the changing nature of statistical methods due to the advancements in computing technology.

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Curves and Surfaces in Geometric Modeling: Theory & Algorithms (The Morgan Kaufmann Series in Computer Graphics) Review

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(More customer reviews)This is a great book, definitely the best among the various books on geometric design and CAGD (other good ones include Farin, Mortsenson, Piegl and Tiller, Hoscheck and Lasser). It is not as encyclopedic as the sources listed above, but it a lot more coherent and a lot clearer, because it follows the unifying concept of blossoming. As a result, one gets multiple complementary views of polynomial curves and surfaces: algebraic, geometric, combinatorial, and algorithmic. For example, we can see where the Bernstein polynomials come from, instead of mysteriously being dropped from the sky. The systematic use of blossoms (polar forms) is particularly elegant in the presentation of surfaces, where it clarifies greatly the differences between rectangular and triangular patches. The discussion of subdivision versions of the de Casteljau algorithm is very thorough and unique. Gallier's book is also the only book to discuss subdivision surfaces in some detail (Doo-Sabin, Catmull-Clark, and Loop). In particular, an analysis of the convergence of Loop's scheme is given. For this, the author gives a remarkable crash course on the discrete Fourier transform. However, this chapter is too dense and should have been split. Also, much more pictures are needed. It seems that the author was in a rush. The appendix on vector spaces is gorgeous, and the one on differentials is also excellent. This book is highly recommended to mathematically inclined readers interested in geometric modeling and computer graphics. Too bad that applications to medicine such as organ modeling, or to computer animation, are not presented. Nevertheless, Mathematica code is provided for most of the algorithms. A web site would be helpful.

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Curves and Surfaces for Geometric Design offers both a theoretically unifying understanding of polynomial curves and surfaces and an effective approach to implementation that you can bring to bear on your own work-whether you're a graduate student, scientist, or practitioner.

Inside, the focus is on "blossoming"-the process of converting a polynomial to its polar form-as a natural, purely geometric explanation of the behavior of curves and surfaces.This insight is important for far more than its theoretical elegance, for the author proceeds to demonstrate the value of blossoming as a practical algorithmic tool for generating and manipulating curves and surfaces that meet many different criteria.You'll learn to use this and related techniques drawn from affine geometry for computing and adjusting control points, deriving the continuity conditions for splines, creating subdivision surfaces, and more.

The product of groundbreaking research by a noteworthy computer scientist and mathematician, this book is destined to emerge as a classic work on this complex subject.It will be an essential acquisition for readers in many different areas, including computer graphics and animation, robotics, virtual reality, geometric modeling and design, medical imaging, computer vision, and motion planning.
* Achieves a depth of coverage not found in any other book in this field.* Offers a mathematically rigorous, unifying approach to the algorithmic generation and manipulation of curves and surfaces. * Covers basic concepts of affine geometry, the ideal framework for dealing with curves and surfaces in terms of control points.* Details (in Mathematica) many complete implementations, explaining how they produce highly continuous curves and surfaces.* Presents the primary techniques for creating and analyzing the convergence of subdivision surfaces (Doo-Sabin, Catmull-Clark, Loop).* Contains appendices on linear algebra, basic topology, and differential calculus.

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Probability, Stochastic Processes, and Queueing Theory: The Mathematics of Computer Performance Modeling Review

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(More customer reviews)This book is suitable for my graduate studies on computer performance. The author directs us from combinatorics, distribution theory, queue theory to queueing networks in a systematic way. I have read the book at ease for its stepwise elaboration of concepts. However, I have also read with hardship as it requires the readers to possess a good command of mathematics, both pure and applied, in order to go through the book.
For a mathematics graduate studying computer networks, I recommend this book. A novice or a mediocrity should pay more patience to read if not yet at a loss.
This book has aroused my interest and eagerness to know more about computer performance from the viewpoint of queueing and networking. In a word, I enjoy reading this book.

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This textbook provides a comprehensive introduction to probability and stochastic processes, and shows how these subjects may be applied in computer performance modelling. The author's aim is to derive the theory in a way that combines its formal, intuitive, and applied aspects so that students may apply this indispensable tool in a variety of different settings. Readers are assumed to be familiar with elementary linear algebra and calculus, including the concept of limit, but otherwise this book provides a self-contained approach suitable for graduate or advanced undergraduate students. The first half of the book covers the basic concepts of probability including expectation, random variables, and fundamental theorems. In the second half of the book the reader is introduced to stochastic processes. Subjects covered include renewal processes, queueing theory, Markov processes, and reversibility as it applies to networks of queues. Examples and applications are drawn from problems in computer performance modelling.

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Analogy-Making as Perception: A Computer Model (Neural Network Modeling and Connectionism) Review

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(More customer reviews)Melanie Mitchell's analogy-making as perception is a remarkably original book. It documents an artificial intelligence project known as copycat, which was implemented as the author's PhD project under Douglas Hofstadter.
Copycat is unlike anything in artificial intelligence. It is not a symbolic system, neither a connectionist one. The major goal of the project is to study the nature of concepts. Concepts, as we all know, are flexible, context-sensitive creatures. For instance, DNA has nothing to do with a computer program, but there is a sense on which we can see DNA as a computer program that guides embrionary development. DNA can also be seen as a zipper, as it "zips down" in two parts (for cell reproduction). Still another view would be DNA as a will, for it carries valuable hereditary "property". Now, DNA is in truth just a molecule, and nothing else. The question is, how can we see the same thing (such as DNA) as so many different things? Moreover, how can these fluid context-sensitive concepts be implemented in rigid, rule-obeying computers?
To which the answer is: what we view is the abstract roles that DNA plays in embrionary development, cell division, and in individual reproduction. And this is the very idea of "Analogy-making as perception".
Well, not so fast. The copycat project is not designed to grasp such extremely complex subjects as DNA, but, on the other hand, it presents a computational architecture that suggests what the nature of concepts is like, and how flexible concepts may emerge from inflexible mechanisms.
Copycat can solve analogy problems such as abc->abd:ijk-> ?. But it is not restricted to trivial ones. Consider the following analogy: abc ->abd:xyz->?. How would you solve it? How do you think that copycat solves it?
Obviously, this project doesn't fit in very easily in classical artificial intelligence, as it attacks some of the most pervasive ideas of the field, such as the separation of perception and cognition. In fact, I think this book redefines the major questions of artificial intelligence (and although Mitchell does not state it, I think the copycat model does not fall prey to either the frame problem or to the symbol grounding problem).
It is very unfortunate that this is not one of the best-selling books in AI. But I believe that it will ultimately make its mark on the History of the field, if for no other reason than it simply is the right approach to genuine intelligence and authentic understanding.
Should one day Amazon.com let me give a 6-star to a book, but charge me a dollar for giving it, this is one that would definitely deserve to be such a 6-star.
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PS. I would also recommend Hofstadter's Fluid Concepts and Creative Analogies; and Robert French's Subtlety of Sameness.

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The psychologist William James observed that "a native talent forperceiving analogies is... the leading fact in genius of every order." Thecentrality and the ubiquity of analogy in creative thought have been noted again andagain by scientists, artists, and writers, and understanding and modeling analogicalthought have emerged as two of the most important challenges for cognitivescience.Analogy-Making as Perception is based on the premise that analogy-making isfundamentally a high-level perceptual process in which the interaction of perceptionand concepts gives rise to "conceptual slippages" which allow analogies to be made.It describes Copycat - a computer model of analogymaking, developed by the authorwith Douglas Hofstadter, that models the complex, subconscious interaction betweenperception and concepts that underlies the creation of analogies.In Copycat, bothconcepts and high-level perception are emergent phenomena, arising from largenumbers of low-level, parallel, non-deterministic activities. In the spectrum ofcognitive modeling approaches, Copycat occupies a unique intermediate positionbetween symbolic systems and connectionist systems a position that is at present themost useful one for understanding the fluidity of concepts and high-levelperception.On one level the work described here is about analogy-making, but onanother level it is about cognition in general. It explores such issues as thenature of concepts and perception and the emergence of highly flexible concepts froma lower-level "subcognitive" substrate.Melanie Mitchell, Assistant Professor in theDepartment of Electrical Engineering and Computer Science at the University ofMichigan, is a Fellow of the Michigan Society of Fellows. She is also Director ofthe Adaptive Computation Program at the Santa Fe Institute.

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Modeling MEMS and NEMS Review

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(More customer reviews)This is among the finest books for modeling MEMS. Most other books give a mish-mash of fabrication and modeling, but you never figure out how to model!! This one is different. This is the gold standard for MEMS books to come.. I picked it up along with many other MEMS books from the library, never thought I would use it, but this is the ONLY book I use. Good even for the the advanced MEMS designer as a quick reference. But great for a new reader who wants to grasp the fundamental of modeling.

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Designing small structures necessitates an a priori understanding of various device behaviors. The way to gain such understanding is to construct, analyze, and interpret the proper mathematical model.Through such models, Modeling MEMS and NEMS illuminates microscale and nanoscale phenomena, thereby facilitating the design and optimization of micro- and nanoscale devices. After some introductory material, a review of continuum mechanics, and a study of scaling, the book is organized around phenomena. Each chapter addresses a sequence of real devices that share a common feature. The authors abstract that feature from the devices and present the mathematical tools needed to model it. They construct, analyze, and interpret a series of models of increasing complexity, then at the end of the chapter, they return to one of the devices described, apply the model to it, and interpret the analysis.In the beginning, the world of microdevices was dominated by experimental work and the development of fabrication techniques. As it matures, optimization and innovative designs are moving to the forefront. Modeling MEMS and NEMS not only provides the practical background and tools needed to design and optimize microdevices but it also helps develop the intuitive understanding that can lead to developing new and better designs and devices.

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