Book Description
This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.
Customer Reviews:
A best book on Statistical Pattern Recognition.......2005-09-13
Multivariate analysis is borrowed to name a NEW subject, Statistical Pattern Recognition (SPR). Many statisticians think it unfair or a shame. In spite of these, it is a good reference book of SPR. :-)
[1] Many contents of this book can be found in any graduate textbook of Multivariate Analysis, for instance, Fisher's linear disciminant, etc.
[2] The book is badly printed. Why not using LaTeX?
[3] Guassian distribution is assumed here and there.
[4] It may be good as a reference book, but definitely not as a textbook.
Standard reference and a classic text but with flaws.......2004-01-20
I do not like to consult this book for the following, quite superficial reason. The book is sloppily produced and proofread
(and the fault is [probably] mainly the publisher's instead of the author's). This manifests itself, e.g., as follows
(1) the typography is flawed (the equations hurt at least my eyes);
(2) at its each appearance, the all-important >
< -sign goes the wrong way.
good coverage for engineers.......2000-08-04
Fukunaga is a standard source for pattern recognition methods often cited in the engineering literature. Covers parametric (particularly linear and quadratic discriminant algorithms) and nonparametric methods (density estimation). It is designed for and popular with engineers. When I was working at Nichols Research Corporation Fukunaga's papers and this book (earlier edition) were often cited as sources to justify the algorithms we used for discrimination problems. In fact Fukunaga had been a consultant to the company (used primarily by the Boston branch of the company where the KENN algorithms were developed). It is a reputable source. I still like Duda and Hart (1972) for good explanations of the fundamental concepts. For statisticians McLachlan's book is now far and away the best source.
Standard Reference in the Field.......2000-04-06
If you are writing a machine learning paper, and need to cite something to support an argument, you can almost always cite Fukunaga. This work is a standard reference in the field. The presentation of most material is very terse, but that is great if you already have a good feel for the material and need to look up some details about some algorithm or technique. There isn't much about neural networks here, but for the rest of the pattern recognition techniques, this is almost always the first place to start. Another strong point for this book is the use of realistic examples, which illustrate many of the statistical techniques.
Book Description
The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.
Customer Reviews:
talk about outdated.......2007-06-27
this book was likely a looker back in the day, but its 2007 now. Need to have better details for non "C"-users. wish i had bought "Idiots Guide to C".
A classic book of numerical algorithms.......2006-12-24
This book, although published 15 years ago, is still very useful. In fact, its more recent counterpart "Numerical Algorithms in C++" is a mess, and I wouldn't recommend it to anyone. The explanations of the algorithms that occur in each section of this book are top-notch. It helps with such questions as "Sure you know how to evaluate an integral with pencil and paper, but how do you do it with a computer?" Everything from linear algebra techniques to integration and evaluation of functions to the FFT and spectral applications are explained clearly and coded up in C. The code is great too, with the exception of one problem that several reviewers have already mentioned - the author has a FORTRAN-like programming style in which each implementation has arrays going from 1 to n versus 0 to n-1. This does cause some implementation problems if you want to transfer the algorithms into another programming language. Overall, though, I can't think of one book that does all of the heavy lifting that this one book does as well as it does in the arena of numerical algorithms.
The book is now available online. Just type "Numerical Recipes" into Google and click on the Numerical Recipes Home Page to peruse the entire book free of charge. You might also find the "Numerical Recipes in C Example Book" useful. That book is simply the source programs that demonstrate all of the Numerical Recipes subroutines. Each example program contains comments and is preceded by a short description of how it functions. I know I found it helpful in many cases.
Very nice book.......2006-08-27
A must buy for students or researchers who need numerical methods. Comprehensive topics. A good place to start to deeper levels. Online book is good for quick look.
A classic, and still worth having.......2006-07-12
"Numerical Recipes" has been a staple in computing libraries for many years, and for good reason. It provides immediately usable implementations of all the workhorses of numerical computation, in production-quality form. Maybe there are better implementations out there, FFTW for example, but getting something to work correctly always comes before getting it to work fast. Numerical computation is a specialty, and vanishingly few of us are specialists. As a result, getting this much specialist knowledge for the price of a very few hours' wage, fully debugged and documented, is a great bargain.
I have to agree with the critics who point out that the Gnu Scientific Library (GSL) is more complete in some areas, and offers better licensing terms. This collection has its own strengths, though, and not just in documentation. The writeup, however, is the major interface between the software and us, the bio-ware. GSL's collection of 'man' (help) pages serves a purpose, but this book's exposition describes a lot more of the background and rationale for the routines. The code and man pages are self-evident statements of the implementation - but "what" is a very different question than "what else" or "why."
This one may not serve all needs. You'd be amazed how many it does serve, though. If you need more than a Matlab session for numerical computing, you need this.
//wiredweird
Great compilation of numerical routines for C programmers.......2004-12-17
I found this book indispensible in my effort to develop profitable trading systems for futures and options and in my research in factor analysis and, more recently, in chronic fatigue syndrome and fibromyalgia. Anyone who programs in C or C++ and works with mathematics must have this book. It covers a surprisingly wide range of algorithms: routines are included for everything from handling Julian dates and solving systems of linear equations to determining eigenvectors and singular value decompositions, solving differential equations, doing numerical integration (quadrature), not to mention calculating fast fourier transforms, lomb periodograms and maximum entropy spectral analyses. While not always state-of-the-art, the routines are quite reliable (when used correctly), clearly-written, and easy to understand and use. I would strongly recommend this book (and the companion software) to anyone who programs in C and is literate in mathematics. I always keep a copy nearby.
Jeffrey Owen Katz, Ph.D.
Author: "The Encyclopedia of Trading Strategies" (McGraw Hill, 2000)
Average customer rating:
- very nice conceptual overview
- Not for the practitioner
- Trash
- Excellent Introduction, Sparse on Details
- A Good Introductory Survey
|
Scientific Computing
Michael T. Heath
Manufacturer: The McGraw-Hill Companies, Inc.
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ASIN: 0072399104 |
Book Description
Heath 2/e, presents a broad overview of numerical methods for solving all the major problems in scientific computing, including linear and nonlinear equations, least squares, eigenvalues, optimization, interpolation, integration, ordinary and partial differential equations, fast Fourier transforms, and random number generators. The treatment is comprehensive yet concise, software-oriented yet compatible with a variety of software packages and programming languages. The book features more than 160 examples, 500 review questions, 240 exercises, and 200 computer problems. Changes for the second edition include: expanded motivational discussions and examples; formal statements of all major algorithms; expanded discussions of existence, uniqueness, and conditioning for each type of problem so that students can recognize "good" and "bad" problem formulations and understand the corresponding quality of results produced; and expanded coverage of several topics, particularly eigenvalues and constrained optimization. The book contains a wealth of material and can be used in a variety of one- or two-term courses in computer science, mathematics, or engineering. Its comprehensiveness and modern perspective, as well as the software pointers provided, also make it a highly useful reference for practicing professionals who need to solve computational problems.
Customer Reviews:
very nice conceptual overview.......2006-07-22
Wow, people seem to be really split on this book. I had Mike Heath for numerical analysis/scientific computing and he was an excellent instructor, one of the best lecturers I've ever had. (As a consequence, I have a hard time separating the book and the class, so judge accordingly.) The book is based on his lecture notes, though he added some material and didn't cover every topic in the book. Just reading the book is useful to give you an overview of the point behind different methods. The goal of the class for which this book was written is actually quite conceptual. It was to give scientists (that's me: a stats researcher who makes heavy use of numerical computation) and CS people in areas other than scientific computing a leg up. It was only a first class for people in scientific computing, the rough equivalent of intro Physics or intro Probability/Stats for people in those respective majors. However, you *won't* be prepared to "roll your own" from this book. In fact, at the beginning of the semester Heath was very careful to note that if you have the opportunity to use a library function for most numerical programming, you are nuts to roll your own. Why? Numerical algorithms are usually extremely complicated and the authors of the code often spend years developing careful expertise on them. Frequently the formulas used to elucidate a given method are NOT the ones used to implement it. You need error traps, tricks to handle ill-scaling and other special cases, etc. These are things that someone who has a one-semester, superficial understanding of a topic simply won't have. So consider the book on the goals it set: it is an overview of a field. If you want to learn more about any one topic, you have to dig deeper and consult references and other works, but this is a good place to start. For this, the book serves admirably.
Not for the practitioner.......2005-11-17
If you are interested in Scientific computing from the viewpoint of the end user that is the guy who uses the method to solve practical engineering problems then this book is lacking.
Not enough methods in this book to constitute an introductory survey of the field. Every chapter gets heavy dose mathematical treatment, apparently Heath loves his math but for the rest of us it doesnt translate into know-how. Know how to solve equations using computational techniques. Very few derivations to back his mathematical swagger, very few examples (if any) and fewer numerical schemes to solve problems. Many of the chapters receive cursory treatment such as PDE's get about 70 pages of print. Far too little to do anyone any good.
He does talk about interesting issues such as conditioning and error analysis and computer precision and memory issues but it is done from such a superficial viewpoint that one cannot use anything to improve ones code. Not recommended if you want to learn numerical methods even if you have an excellent professor to learn from. His chapter on FFT's was even more abstruse and there was hardly any methods with which to solve PDE's.
I had this for a graduate course in Numerical Methods but ended up using Hoffman's excellent book on Numerical Methods.
Trash.......2005-10-14
If you want to have a solid understanding of numerical computation, this book is definitely the last choice. Many theorems are given without any proof or even intuitions behind them in this book. Even when a proof is provided, it's often far from rigorous. The organization of chapters is the worst I have ever seen, revelant materials are scattered over several different locations rather than put together. Take the SVD for example, it is mentioned in the end of chapter 3, but reappears in chapter 4, which is very confusing. If you are new to this area, please don't read this book. It gives you many many facts without explanations, which I think is not a good way to learn new things. David S. Watkins' Fundamentals of Matrix Computations is a lot better and easier to understand. It also emcompasses many detailed treatments of various theorems. If you have bought Heath's book, don't be sad, at least it can serve as a coaster.
Excellent Introduction, Sparse on Details.......2004-11-20
While sparse on the details of many of the algorithms and theorems mentioned, as an introduction it covers a broad range of material-enough for two semesters of study. The writing is lucid, and when a proof of a theorem is given, it is easy to follow and explained in english afterward. Rationale is given for everything, which is a great benefit to a student not familiar with the nuances of sophisticated linear algebra.
A Good Introductory Survey.......2002-11-05
This book excels at presenting a reader with little to no knowledge in computer science and a mild mathematical background (knowledge of differential equations as a prerequisite) with the fundamental concepts regarding scientific computing. The presentation of pseudo-code algorithms helps smooth the transition from analytical (pencil and paper) thinking to numerical thinking. The algorithms are presented in a manner such tha anyone with access to dozens of possible environments can apply them, though they are by no means complete, thus requiring some thought into the processes. The material covered is 110% of what an engineer will want to know, 90% of what an applied mathematician will want to know, and 45% of what a numerical analyist will want to know. In all, a great book to begin a foray into numerical computing.
Book Description
This highly successful and scholarly book introduces readers with diverse backgrounds to the various types of mathematical analysis that are commonly needed in scientific computing. The subject of numerical analysis is treated from a mathematical point of view, offering a complete analysis of methods for scientific computing with careful proofs and scientific background. An in-depth treatment of the topics of numerical analysis, a more scholarly approach, and a different menu of topics sets this book apart from the authors' well-respected and best-selling text: NUMERICAL MATHEMATICS AND COMPUTING, FOURTH EDITION.
Customer Reviews:
Horrible Book........2007-09-10
Honestly, this has turned out to be a horrible book. Particularly in the disconnect that exists between the text and the problem sets. Very few of the examples are useful to read through because they are trivially simple, while the problem sets seem to take particular delight in finding the hardest tricks to be solve-able.
I would highly suggest that anyone that purchases this book, already know what they are trying to learn, or have an excellent teacher that can fill in the gaps.
Book for the U.......2007-05-29
I bought this book for my college and it was excellent
Great Book.......2006-11-26
I think this book is lucently written and explains various aspects of numerical analysis in great detail. The proofs are stated in an understandable way and algorithms are presented clearly and in such a way that it is easy to implement them in the programming language of one's choice.
Disappointing at best.......2004-01-04
The book was a major disappointment. I am glad that I did not purchase it for my class, but instead borrowed it. The ordering of topics and emphasis choices never seemed to make sense to me. The layout throughout most of the text is like one long, run-on sentence. The underlying structure of numerical analysis never developed and I was left swimming in meaningless details while the basics were short-changed by an over abundance of specialized algorithms. Perhaps the text's curriculum could be saved by a capable professor, but alas my professor was just as scatter-brained as the text. More pictures would also have been helpful. A replacement text I recommend, which covers the first, matrix theory portion of this book, is David S. Watkins' Fundamentals of Matrix Computations.
Book Description
This is the greatly revised and greatly expanded Second Edition of the hugely popular Numerical Recipes: The Art of Scientific Computing. The product of a unique collaboration among four leading scientists in academic research and industry Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, this new edition remains the most practical, comprehensive handbook of scientific computing available today. Highlights of the new material include: -A new chapter on integral equations and inverse methods -Multigrid and other methods for solving partial differential equations -Improved random number routines - Wavelet transforms -The statistical bootstrap method -A new chapter on "less-numerical" algorithms including compression coding and arbitrary precision arithmetic. The book retains the informal easy-to-read style that made the first edition so popular, while introducing some more advanced topics. It is an ideal textbook for scientists and engineers and an indispensable reference for anyone who works in scientific computing. The Second Edition is availabe in FORTRAN, the traditional language for numerical calculations and in the increasingly popular C language.
Customer Reviews:
Outstanding reference book on numerical algorithms.......2007-04-24
This is the single best book that I have found for teaching numerical methods in science and engineering to upper division undergraduates and graduate students. Students often comment that this should be the selected text even in the programming course because it provides both an overview of the methods and examples that demonstrate the application. The discussions are excellent and the Fortran 77 programs easy to follow even if one is more familiar with C or C++. You should not purchase the Fortran 90 version of this book without getting this book as well because the Fortran 90 book does not contain the excellent discussion of the methods and procedures. Rather it references this book for discussion and simply provides the F90 versions of the routines.
Proprietary source the Achilles' heel for non-students.......2002-12-03
I first bought this text in 1994 while doing scientific programming for graduate school work. A fellow graduate student had suggested I use an undocumented routine that (I later discovered) came from Numerical Recipes (NR). I was impressed enough with NR's presentation of ideas that I also bought the example book ISBN 0521437210 (which I've hardly cracked since) and a diskette of source code (which cost as much as the book but worth it). I was able to do a lot of basic research quickly with NR code, and I still occasionally use NR's routines.
The authors have certainly done a good job assimilating a lot of material. Since other reviewers have done well to highlight the importance and utility of this landmark book, there is no need to repeat those sentiments here. However, to this title's detriment, the authors consider their book to be a proprietary library of source code more valuable than the explanatory text discussing it (one can in fact download the text on-line though it's hardly worth the hassle). This perception is ironic since the authors confess that "the lineage of many programs in common circulation is often unclear" (p.xviii), and many details of presentation, ideas, and algorithms are clearly "borrowed" from other excellent (some now out-of-print) numerical methods books or journals.
I often wondered why NR routines occasionally adopted bizarre and/or obviously inefficient programming structures - over time I decided that this was probably done to make these algorithms appear as so not to clearly violate other published material. As a student, NR's legal disclaimers regarding derivative works (p.xvi) never bothered me and I was willing to overlook the sometimes unpolished source code insofar as it functioned properly. However, as a professional I now find the lack of fair-use provisions on the uncompiled source way too restrictive to rely on these routines in good conscience (I have to buy another textbook or license for every soft copy or machine upon which the source code resides!). I suspect this policy ultimately hurts NR's textbook sales: it would be nice to able to use and pass along the source code between professional colleagues without restriction because most would certainly buy (if they don't already own) the textbook to understand what the source does (just as I did). Source code used in scientific programming is practically worthless without proper documentation, and there's no better documentation than a full length textbook!
I have since expanded my numerical methods library to other references supporting true public-domain codes. With an expanded basis of comparison, I regret to say that I am becoming less and less impressed with NR's implementations and explanations. I am finding many of NR's algorithms to be inefficient or unnecessarily approximate, and - on rare occasion - buggy. There have been quite a few bugs uncovered over the years, and the NR web site has done a good job of keeping track of them (although I know of at least one bug uncorrected by NR to this day).
This book is excellent for students wanting a good reference for quick and dirty types of analyses or scientific computing. Professional programmers, scientists, engineers, specialists or analysts performing software development for laboratory or scientific research would be well advised to reference this title, but ultimately they will likely need to rely other resources if they require efficient and/or unrestricted (public-domain) source codes for their work.
(P.S. - A reviewer elsewhere noted that the "quality of the binding was terrible" and I've also found this to be the case. My hardcover is literally had to be taped on after a few years of use.)
A Useful Tool for Programmers, Researchers, and Students.......2002-07-05
This book contains hundreds of "canned codes" in the FORTRAN language. The book provides several variations of many popular numerical techniques and provides the most stream line (comp. time) codes available. Most codes allow for optimization to be build in, such as an RK4 (4th Order Runge-Kutta) with variable steps sizes. Great if you don't want to write your own code for a subroutine, or it you just don't know the method well enough to write it yourself. The book also provides some basic explaination of the techniques and codes with is very helpful so that the code is less of a black box, although its not that detailed.
There is also a CD available that has the codes already written and ready to go. I prefer to type it in on my own, or just make my own because it gives a better udnerstanding of what the code is doing. The biggest turn-off for me is that some codes have subroutines upon subroutines which can make things a mess.
All around a useful tool for programmers, researchers, and students.
Indispensible, a classic in the field.......2001-07-10
This volume, and its companions for other programming languages, is an absolute classic. The authors strike the right balance between cookbook solutions and theory, so that most of us get just enough background to choose the right algorithm but not so much to get drowned in theory. This edition is the first devoted only to Fortran, but is the second edition published by the authors. It includes a number of additions and corrections, many of which appeared in Computers in Physics (now the journal Computing in Science and Engineering published jointly by the IEEE and the APS). My only criticism is, where were these books twenty years ago when I needed them? I would recommend these books to anyone involved in the application of numerical methods. They are tremendous time savers.
I never bothered with the discs, as most of the routines are fairly short and not a problem to type in, but I recommend the companion example books to help get the routines running.
Routines an more routines.......2001-07-05
If you ever had to program a complicated numerical algorithm, such as SVD decomposition, Bessel functions, eigensystems or Fourier transform, you will know how useful this book is. All those problems, and many others, are presented, the theory is explained and the full code of a routine, which solves it, is given. This version brings the codes in FORTRAN 77, but there are versions for Pascal, C++ and Basic. If you need any routine, you just have to "cut and paste" it from the book into your program.
Customer Reviews:
An excellent book for scientists and engineers.......2006-02-10
The book "Engineering and Scientific Computing" in Scilab,
presents clearly the elements of the Scilab language.
A scientist with some programming background,
even elementary, can readily learn and exploit the
elegant and compact Scilab scientific programming
environment.
However, the strongest point of the book is its tutorial
value. The reader can through the Scilab tool,
improve the knowledge of important signal processing topics,
exploit algorithms for the numerical solution of ODEs and
tackle with optimization problems. Furthermore, the book contains
excellent material on SCICOS (the dynamical system builder
that accompanies Scilab) and a lot of applications.
The CD that is included with the book is also very helpful.
The presented scientific applications are aimed mainly
to the advanced scientists and engineers, although a large part
can be utilized and by undergraduate students of an intermediate
level.
In summary, I liked and enjoyed this book and I strongly
recommend it to all the scientific/engineering community.
Fine book on an excellent software.......2001-07-11
This is a good book describing an excellent free scientific Matlab-like software package available for many computing platforms. It complements well the extensive on-line help of the software and the information available on the Web.
The first three chapters gives a condensed overview of the software. I found the description of the graphics capabilities particularly useful as a reference. The next two chapters describe the use of the software for linear algebra, polynomials, linking to C and FORTRAN, and more advanced aspects. The remaining chapters concern tools and applications mainly of a system oriented nature. The tools are generally of a very high quality and accuracy, but of course slower than in compiled languages.
The book would have been been even more useful if it included more information on how to customize the software and a more comprehensive index. Also, the linking to C and FORTRAN routines does not appear to be completely simple.
Book Description
The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a comprehensive text and reference work on scientific computing. Thoroughly self-contained, it proceeds from mathematical and theoretical considerations to actual, practical computer routines. This new version incorporates completely new C++ versions of the more than 300 Numerical Recipes Second Edition routines widely recognized as the most accessible and practical basis for scientific computing, in addition to including the full mathematical and explanatory contents of Numerical Recipes in C. Key Features:
Includes linear algebra, interpolation, special functions, random numbers, nonlinear sets of equations, optimization, eigensystems, Fourier methods and wavelets, statistical tests, ODEs and PDEs, integral equations, and inverse theory.
A wealth of tricks and tips for scientific computing in C++
The routines, in ANSI/ISO C++ source code, can be used with almost any existing C++ vector/matrix class library, according to user preference
Includes a simple class library for stand-alone use Other new Numerical Recipes products for your library...
Numerical Recipes Example Book [C++]
Numerical Recipes Code CDROM with Windows, DOS, or Macintosh Single Screen License--v2.10 including C++, Second Edition
Numerical Recipes Code CDROM with LINUX or UNIX Single Screen License v2.10 including C++, Second Edition
Numerical Recipes Code CDROM with Windows, DOS, or Macintosh Single Screen License
Customer Reviews:
Definitive book for scientific computing.......2007-06-29
Forget about the bad comment about this book. Those guys do not understand scientific computing at all.
About C style functions and C++ classes: for speed, C style functions still take the lead. The method provided here considered speed seriously. even in vector wrapping, reference trick is used everywhere to max speed.
If you are professional programmer, you will appreciate the careful design in vector and matrix. I am not saying it is perfect. There are better ways to handle vector and matrix more consistently, like boost lib. but within the limited scope of this book, the care for details is just incredibly good.
And the extra charge for the typed program, it is worth it. So stop complaining please.
Disappointed.......2007-03-12
I returned this book. The licensing is very restrictive. The book comes with an "Immediate License" that allows you to type the routines into your computer and use for personal and noncommercial purposes. Any other use or distribution requires the purchase of an additional license.
Some of the routines (Quicksort, p 336) are not very well coded: a bunch of one letter variable names, loops that only exit on break.
This book contains recipes in C piled together in one class, DON'T BUY.......2007-01-25
Book contains the same numerical procedures as in recipes in C piled up in one class. Using this book is like using C without exploitation of object capacities on C++.
What I would recommend is to get the vector library Blitz++ and Numerical recipes in C; both are available for free on-line. Using Blitz++ you achieve speed of Fortran 90 and easy exposition of C++.
And that's what author of this book should have done, write all the procedures based on blitz++.
This book uses C++ only in its title.
context not included.......2006-11-21
Have only read over the first four chapters but so far the book seems to be little more than a print out of minimally commented source code with no context as to how/where/why one would apply the code or even explanations of what the code is doing. All code seen so far relies heavily on the included classes so the 'guts' of the recipe is not transparent with in the chapter. Sample user input and program output are not always listed with code but rather at the end of the chapter.
Still may prove to be good reference.
C++ Recipes.......2006-11-03
It is very helpful in scientific computing. I recommend it for an experienced programmer. If you are a fledgling, get another book to complement it.
Book Description
A large number of scientists and engineers employ Monte Carlo simulation and related global optimization techniques (such as simulated annealing) as an essential tool in their work. For such scientists, there is a need to keep up to date with several recent advances in Monte Carlo methodologies such as cluster methods, data- augmentation, simulated tempering and other auxiliary variable methods. There is also a trend in moving towards a population-based approach. All these advances in one way or another were motivated by the need to sample from very complex distribution for which traditional methods would tend to be trapped in local energy minima. It is our aim to provide a self-contained and up to date treatment of the Monte Carlo method to this audience. The Monte Carlo method is a computer-based statistical sampling approach for solving numerical problems concerned with a complex system. The methodology was initially developed in the field of statistical physics during the early days of electronic computing (1945-55) and has now been adopted by researchers in almost all scientific fields. The fundamental idea for constructing Markov chain based Monte Carlo algorithms was introduced in the 1950s. This idea was later extended to handle more and more complex physical systems. In the 1980s, statisticians and computer scientists developed Monter Carlo-based algorithms for a wide variety of integration and optimization tasks. In the 1990s, the method began to play an important role in computational biology. Over the past fifty years, reasearchers in diverse scientific fields have studied the Monte Carlo method and contributed to its development. Today, a large number of scientisits and engineers employ Monte Carlo techniques as an essential tool in their work. For such scientists, there is a need to keep up-to-date with recent advances in Monte Carlo methodologies.
Customer Reviews:
An excellent book on Monte Carlo.......2006-05-04
Jun Liu has been a prominent researcher in MCMC since the mid 90's. His research has contributed a great deal to the development of Gibbs sampler, sequential Monte Carlo, weighting/importance sampling, missing data, and MCMC related applications in Bioinformatics. Not surprisingly, this book has them all, plus many other interesting topics. The final two chapters review some of the theories. This book has a strong flavor in statistical physics, which I like very much. It also contains some applications in, for examples, engineering (e.g. nonlinear filter, sequential Monte Carlo), biology (DNA sequencing), image analysis (clustering) and stochastic optimization.
Jun Liu presents things very clearly and concisely, and hopefully you can benefit from his book.
An awesome book on Monte Carlo methods.......2005-09-13
Now, I am reading this book. I would like to mark it 4.5 stars if possible.
[1] The author is an expert of computational statistics and Bayesian analysis, an active mathematician at Harvard.
[2] The background of this book is related to bioinformatics, physics, etc, which puzzles me a lot while reading.
[3] You can find the author's deep understanding of MC methods throughout the book.
[3] It is suitable for the graduate students of statistics.
[4] It's a little bit pity that this book is not purely written for mathematicians. Anyway, it is a witness of MC methods in development.
Solid theory in Monte Carlo, but less application examples.......2005-08-22
Solid theory in Monte Carlo, but less application examples
A First Rate Book on MC.......2001-08-07
The author is a top young gun from Harvard's Statistics Dept., and is an expert in many applied areas that utilize Monte Carlo, like the red hot bioinformatics. This book covers MC techniques developed in many different fields e.g., physics,structural biology, statistics. It has a wide range of examples, some of which are very new (e.g., bioinformatics) and non-standard. It contains many interesting ideas, and is concise mathematically and easy to read. Highly recommended.
Book Description
Scientific Computing and Differential Equations: An Introduction to Numerical Methods, is an excellent complement to Introduction to Numerical Methods by Ortega and Poole. The book emphasizes the importance of solving differential equations on a computer, which comprises a large part of what has come to be called scientific computing. It reviews modern scientific computing, outlines its applications, and places the subject in a larger context.
This book is appropriate for upper undergraduate courses in mathematics, electrical engineering, and computer science; it is also well-suited to serve as a textbook for numerical differential equations courses at the graduate level.
* An introductory chapter gives an overview of scientific computing, indicating its important role in solving differential equations, and placing the subject in the larger environment
* Contains an introduction to numerical methods for both ordinary and partial differential equations
* Concentrates on ordinary differential equations, especially boundary-value problems
* Contains most of the main topics for a first course in numerical methods, and can serve as a text for this course
* Uses material for junior/senior level undergraduate courses in math and computer science plus material for numerical differential equations courses for engineering/science students at the graduate level
Customer Reviews:
Does the job well.......2001-06-30
This book is an excellent introduction to the field of scientific computing and serves well as a textbook, given the many exercises included in it. Although the software packages quoted in the book have been considerably revised since the time of publication of the book, one can still use it effectively as a guide to the construction of algorithms and software for scientific applications. The level of the book makes it suitable for a course in numerical analysis at the advanced undergraduate level. After a brief review of the concepts and strategies employed in mathematical modeling in chapter 1, the author begins in chapter 2 with the study of initial value problems for ordinary differential equations. He motivates the discussion with the predator-prey problem from mathematical biology and the ballistic trajectory problem with air resistance from physics. The initial-value problem for the general case of systems of ordinary differential equations is then solved using finite difference methods. The author treats thoroughly Euler's method along with its discretization error. Recognizing that first-order methods have very slow rates of convergence, Runge-Kutta methods are discussed next to alleviate this problem. The Heun method, fourth-order method, and more general one-step methods are discussed in detail. The sample initial value problems are then treated using some of these techniques. The technique of polynomial interpolation, so popular as a solution technique in high-level symbolic programming languages such as Mathematica, is discussed in this chapter also. Multistep methods, such as the Adams-Bashforth, Adams-Moulton, and predictor-corrector methods are treated also. The author also discusses the important concept of stability in this chapter. Although he does not give a rigorous definition of stability, due to the mathematical formalism needed for such a definition, he does give several examples of differential equations that are not stable, and also examples of instabilities in the actual numerical methods employed.
Boundary value problems for ordinary differential equations are treated in the next chapter. The author motivates the problem via a two-point boundary value problem, but only concentrates on linear boundary value problems in this chapter, with the nonlinear case treated in chapter 5. The author carefully distinguishes between Dirichlet and Neumann boundary conditions. The solution of the discretized problem is solved appropriately with Gaussian elimination, and the author gives a numerical example. The case of periodic boundary conditions is also treated, and the author chooses to solve the resulting linear system using the Sherman-Morrison technique, instead of Gaussian elimination, arguing (correctly) that this method only needs code for solving tridiagonal matrices.
The study of the solution of linear systems of equations is taken up in more detail in the next chapter, with emphasis on solution techniques for banded or full matrices. The class of least square problems is treated first, with least square polynomials and their calculation using orthogonal polynomials. The author then treats Gaussian elimination in more detail in this chapter, with treatments of LU factorization and banded matrices being treated. The author gives the reader more details on the performance issues involved in the different solution techniques. Ill-conditioning and error analysis are first discussed here in the context of solution of systems of linear equations, along with definitions and calculations of condition numbers. The author also gives good overviews of alternative factorization techniques, such as Cholesky and QR factorization.
The most important application of numerical methods is in the class of nonlinear problems, since these usually do not have analytical solutions. Even if analytical solutions are found in terms of special functions, the calculation of these special functions typically must be done using techniques from numerical analysis. Nonlinear problems are discussed in chapter 5 of this book, wherein the author again uses the projectile problem to introduce shooting methods. This is followed by a very detailed discussion of the solution of a nonlinear equation using bisection, secant, and Newton's methods. Systems of nonlinear equations are then discussed, with the infamous Picard iteration technique leading the discussion, followed again by a treatment using Newton's method.
Then in the next chapter, the author switches gears somewhat by moving away from techniques based on finite differences and discussing ones such as finite element, Galerkin's and Rayleigh-Ritz methods. The mathematical considerations employed in this chapter are a little more involved than the other chapters, but the author explains the ideas well, and the assigned exercises shed more light on the issues involved. Spine approximations are also discussed, along with the numerical evaluation of the integrals that naturally arise in Galerkin methods.
Eigenvalue problems, so ubiquitous in all areas of science and engineering, are the subject of the next chapter. Interestingly, the author discusses Gerschgorin's theorem, which usually does not appear in a book at this level. Most of the popular techniques for solving eigenvalue problems, such as QR and iterative methods, are discussed thoroughly.
The author gives the reader a taste of the numerical solution of partial differential equations starting in the next chapter, where the heat equation, wave equation, and Poisson's equation lead off the discussion. Separation of variables is discussed briefly as a technique of solution, but the author places emphasis first on finite difference methods for solving these equations. The stability and error analysis of these methods are first studied for the heat and wave equations, and this is followed by a discussion of implicit methods, with a brief treatment given of the Crank-Nicolson method. After a discussion of semi-discrete methods, the author then moves on in last chapter to problems in dimensions two and three. The sparse matrices arising from the discretization of the problems are shown for the Poisson and heat equations. The ADI method, along with Gaussian elimination, Jacobi's, Gauss-Seidel, SOR, and conjugate gradient methods are all given fine treatments.
Average customer rating:
- strong computational emphasis
- Great Suppliment to Numerical methods
- Python for Science Academics and Engineers, NOT programmers
- Convincing demonstration of Python's value in science
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Python Scripting for Computational Science (Texts in Computational Science and Engineering)
Hans Petter Langtangen
Manufacturer: Springer
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ASIN: 3540294155 |
Book Description
The goal of this book is to teach computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs (scripts) written in the easy-to-learn, high-level language Python. The focus is on examples and applications of relevance to computational scientists: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping old programs with graphical user interfaces; making computational Web applications; and creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran. In short, scripting with Python makes you much more productive, increases the reliability of your scientific work and lets you have more fun - on Unix, Windows and Macintosh. All the tools and examples in this book are open source codes. The third edition is compatible with the new NumPy implementation and features updated information, correction of errors, and improved associated software tools.
Customer Reviews:
strong computational emphasis.......2006-11-13
Langtangen's emphasis here is on a reader who comes from a strong background in engineering or science, and is familiar with common computational ideas and has done some programming, but not necessarily in Python. The typical book on Python is aimed at a general programming reader, and the examples in such a book usually are quite elementary, from a computational viewpoint.
The merit of Langtangen's book is that he gets into a lot of computational ideas. This is not a trivial book. Aspects like parsing data in files, connecting to local and remote hosts, and interacting with programs written in other languages are covered. For the latter, the important cases of Fortran and C programs are explained. The choices of these languages is deliberate. In science and engineering, they are the dominant languages for raw computation. And you are likely to have legacy code written in these, that you cannot abandon while using Python.
Great Suppliment to Numerical methods.......2006-07-25
When I first got ahold of this book I had just finished learning all the gory details of good numerical codes. But when developing tests for simple cases I found that development went way too slow, so someone suggested I learn Python. This book provides a great demonstration of how python can supplement your existing codes. Either by organizing the tests, formatting output, or just adding pretty interfaces.
This book contains a lot of the necessary extras that a scientist or engineer must do to get his work going or finished, which is too pedantic to be taught in most courses. It shows the power of Python over some other scripting languages for this purpose. It is definitely one of the best references on my book shelf.
Python for Science Academics and Engineers, NOT programmers.......2005-06-03
I bought this book as an experienced programmer and Unix user expecting more of a "Numerical Recepies in Python" emphasis on the efficient implementation of algorithms which happen to be in Python. I should have paid more attention to the description.
This book is really more of a "Grad Student's Guide to Everyday Python Usage". I imagine it would be very valuable to a mathematics Grad student without too much programming or shell experience, looking for an alternative to Matlab. However, there is very little "Computational Science" in this book. Do NOT expect a cookbook of high performance algorithm implementations.
The book is a very verbose 700+ pages, all in an unexciting academic LaTeX format. The author works through idiom after idiom for accomplishing different tasks in fairly stand-alone sub-sections without much of a feeling of conceptual "flow" between them. It sort of feels like reading through the author's personal lab notes that he took everytime he learned a new language feature or trick.
If you are an experienced programmer, you will quickly get impatient with the verbose presentation that emphasizes idioms and examples instead of fundamental concepts and syntax reference tables. But, if you are an experienced programmer, you are not the target audience for this book.
Braddock Gaskill
Convincing demonstration of Python's value in science.......2004-10-15
The author has 2 main goals:
1) To improve the productivity of scientists familiar with specific software systems (especially Matlab, Maple, and Mathematica) by teaching them to "glue" applications together.
2) To advocate Python as the preferred "glue" language. In his own words, "I hope to convince computational scientists having experience with Perl that Python is a preferable alternative, especially for large long-term projects."
He has certainly done a creditable job. As an expert in computational differential equations, he neglects neither efficiency nor correctness, while stressing both simplicity and reliability. In this sense, he has done a great service to the Python community.
The question is: What justifies the purchase of his book?
The answer is: Chapters 4, 9, and 10.
Contents:
1. Introduction--26pp
Very convincing arguments.
2. Getting Started With Python Scripting--38pp
Interesting examples.
3. Basic Python--56pp
A too-quick tutorial. Go to python dot org instead.
4. Numerical Computing in Python--48pp
Stellar explanations of vectorized array operations.
5. Combining Python with Fortran, C, and C++--36pp
Details use of Fortran2Py and SWIG. Mentions many alternatives.
6. Introduction to GUI Programming--70pp
Useful examples of Tkinter/pmw widgets.
7. Web Interfaces and CGI Programming--24pp
Good source of ideas.
8. Advanced Python--132pp
Deep and extensive. Includes: option parsing, regular expressions, data persistence and compression, object-oriented programming, exceptions, generic programming, efficiency.
9. Fortran Programming with NumPy Arrays--32pp
All about efficiency and re-use.
10. C and C++ Programming with NumPy Arrays--40pp
More about efficiency. NumPy C API, C++ objects, and SCXX.
11. More Advanced GUI Programming--73pp
Tedious discussion of both Web and standalone GUIs. BLT, canvas, cgi.
12. Tools and Examples--70pp
Excellent examples of PDE solvers, with a powerful GUI, but quite long and tedious.
A. Setting up the Required Software Environment--16pp
Wonderfully specific installation instructions!
B. Elements of Software Engineering--50pp
Python's strength! Very practical advice on modularity, documentation, coding style, regression-testing, version-control.
Strengths:
+ Downloadable py4cs package, esp. numpytools module
+ Great advice everywhere, e.g. CGI checklist, Pythonic programming, and trouble-shooting.
+ Concrete evidence for most assertions.
+ Very attractive presentation. Sturdy, high-quality cover, binding and pages. Brief, elegant code fragments (except in Chapter 12). Readable prose. No wasted space.
+ Available as 5MB pdf file, after purchase of hardcopy. Very nice.
+ Slides, installation instructions, and errata also at web site. Very professional.
My peeves:
- Not enough tables to be a useful manual.
- On p.428(#7) he points out that handling a raised exception is very slow. However, when I time his example with a positive argument, the try-except version is 20% faster (b/c the if clause is skipped), so he is actually giving bad advice for the general case. Luckily, he contradicts himself later, on page 685: "Exceptions should be used instead of if-else tests." The best advice: Avoid common exceptions in inner loops.
- The 10-page index is not as great as it at first seems. (See Martelli's Python in a Nutshell for a better one.)
- Pure interface functions should 'raise NotImplementedError', rather than 'return'.
- Exceptions should never be trapped mindlessly with 'except:'. That would hide your own SyntaxErrors!
- Too many exercises. (It's published as a textbook.) Since there are no answers, the exercises are useless for non-students. (See Lutz's Learning Python for effective exercises with answers.)
Overall rating:
This contains the best information on numerical programming in Python that I've seen. Though expensive, it could easily be your only Python book, given the excellent online documenation already available.
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