Simulation Books

I found a Visual Guide for the Mac confusing the author assumes you already know what the tools are and where they are located. I was also flustrated by working thru more than one example only to find you had to leave Illustrator to perform a task in Adobe Streamline. Well guess what I don't have Adobe Streamline. I found Adobes Users Guide and tours far more informative. A Video would get this information across far better.

Regarding Adobe Illustrator: A Visual Guide for the Mac - A step-by-step approach to learning illustration software...

Inspiring projects, of which you probably would need to know the program before tackling them.

I'm only surprised and disappointed that these two excellent authors, Janet and Linnea have not done a revise as Adobe Illustrator 8 is way new and greatly improved since 1995. Hence only 4 stars, but 5 thumbs up if they ever released a revise!

If you're looking for projects to do, and you know your way around Illustrator, this is a great book to pick up for your library.

This book is one of my favorite graphic design books. I can't believe no one else has written a customer review! "Adobe Illustrator:A Visual Guide for the Mac" shows examples of things you need to do in Illustrator, and how to get them done. The authors make skillful use of the step-by-step approach. The examples and projects are visually appealing and the instructions are clear and easy to follow. I especially like the section on creating vector art with a hand drawn look, "Softening the postscript line," and the section on manipulating and coloring clip art,"Modifying clip art." Although this book was written in 1995 for version 5 or 6, the instructional techniques work just fine in Illustrator version 7. This book is simply jam-packed, from cover to cover, with useful advice, ideas, and detailed instructions for creating and working with vector art. I highly recommend it as a must-have for any desktop publisher, graphic designer or digital illustrator.

First the good: The first three chapters gives the reader a nice introduction to using R for Bayesian statistics and some well worked out examples: a necessity when dealing with a program that one is unfamiliar with. The text does a decent job of complementing the material found in another text on basic Bayesian methodology such as Gelman et al. (2004) or Carlin and Lewis (2008).

The Bad: Towards the latter half of the text the author begins to use a program from the 'Learn Bayes' package entitled "Laplace". It is of my belief that this black box is faulty and very badly behaved. Many of the examples from the text as well as exercises from the sections would not run simply because of this black box. None of nine graduate students working together and independently were able to get this function to perform its duties on a regular basis.

The Ugly: Use of a black box programming technique does nothing more than to add to confusion of how Bayesian methodology works and does not give the reader an adequate background on how to program R to perform Bayesian methods. Black box usage trains a budding statistician to point, click, type, and look at results without really giving them the necessary tools to know if what they are view is even reasonable or what they wanted in the first place.

Conclusions: Decent at first; weak at the last. Would not purchase again as a reference to R.

Great book. If you work through the examples, this book will move you to very near the top of the R learning curve and, more importantly, race you to the peak of the Bayesian curve.

Jim Albert is a great teacher and an excellent writer. The R language is becoming one of the most used languages by statistical researchers. This is because it has many similarities to S and can be used freely, Jim makes R easy to learn for statisticians in this book. One of the big breakthroughs in Bayesian statistics over the past 2 decades was the implementation of complicated priors and hierarchical models through the Markov Chain Monte Carlo (MCMC) algorithms. The leaders is this filed created free software called BUGS (for Bayesian Analysis Using Gibbs Sampling). Gibbs sampling is one of the most commonly used MCMC algorithms. Statisticians using this software have been able to provide more satisfactory solutions to many basic and complex problems using these tools. After Windows became the dominant operating system on personal computers WINBUGS was born. This is a version of BUGS that uses Windows as the operating system and takes advantage of Windows many nice features. Now for the first time to my knowledge Jim Albert show the reader how to incorporate the BUGS technology in the framework of R programming. This can only add to the practical use of Bayesian methods among statisticians for research that advances both the theory and applications. In the late 1990s I was working in the medical device industry where a number of clinical trials were being analyzed using the MCMC methods. Jim deserves a great deal of credit for moving Bayesian statistics into the framework of R!

One of the most influential books on MD and MC, a little outdated by the year, a second edition would be welcome.

It is a good book in general, but i feel it assumes an intermediate to advanced level of knowledge of the subject.

Quite often the explanations are not elaborate enough to understand the method discussed. A slightly more elaborate treatment would make this a first rate book.

It's the book to have if you want to learn computer simulation. It covers both Monte Carlo and Molecular Dynamics, from the start for the beginner. It also includes more advanced topics, but is definitely focused on studying fluids and not solids. The code is written in Fortran. If you program in C I suggest Rapaport's book, but this one is a definite must read.

This is an excellent book for anyone who has solid knowledge of ODEs and wants to learn how to apply that knowlege to computer applications connected with ODEs. The book is useful... You can learn to use ODEs in a manner that is beyond the standards lectures--ODEs come alive with Maple V. Anyone with a love for ODEs will love this book.

This book is great and it is useful to anyone with basic to advanced knowledge of ODE's & PDE's. Learn to graph, plot, and solve complex ODE's with Maple V in seconds. Things like: Directional Fields, Numerical Approx, 2D and 3D plots of ODE's and more. Great for math professionals and students.

This book is one of the best sources of information on differential equations I have seen. I took a differential equations course many years ago. Now that I need to understand the mathematics in financial derivative pricing, I find that the root are in differential equations. This book gives a great review of differential equations to owners of Maple. The examples are excellent. However, the book is plagued with typos that cost me a lot of hair and time. Otherwise, I recommend this book to anyone who wants to review this topic and has access to Maple. Not for the beginner though

This book contains alot of information on how SPICE converges on solutions and how to deal with nonconvergence.

Among all the books which I have come across about SPICE, this is about the best book you can find. Similar to any analog circuit design, setting all the optional parameters appropriately is always a game of trade-off among simulation speed, accuracy, and stability. I came across this book while I was working on a crystal oscillator. It has done a decent job in explaining the intricate spice parameters, such as timestep control options and numeric integration methods, without asking the reader to first become a mathematician. Nevertheless, finishing this book does not mean you will be able to solve all the convergence problems. In my opinion, the best approach to understanding spice is to experiment with the settings frequently to gain a true feeling of how they interact. I give it 4 stars because there are a lot of repeititions in the book, meaning that the author would often repeat a point using different sentences. If there is an important concept, state it once or twice, but not three or four times... The repetitive sentences gave me the feeling that he was worried the book might turn out to be too short. Having said that, I still recommend this book to any circuit designer who really desires to make their transient sims fast and accurate.

This book is a must if you plan on doing any serious work with spice. It focus on the convergence of simulation rather than the circuit. So if you are looking for an introductory book, this one might be a bit too heavy. However, I like the CD rom, which includes RSpice and Rgraph. There seems to be no restriction in terms of number of devices used.

Over all, this is a very good book for engineers in practice.

I bought the book two years ago to get up to speed with risk analysis. I'll now buy it again because I had to leave the last copy with my former employer. The book is very well written, clear and concise with good worked examples so that even a non-mathematically-inclined person and statistics novice will readily grasp the basic concepts and be able to use the enclosed software CD. Very good! My one only gripe is that the demo software included with the book is limited to a "student" level of performance, but I guess if you get heavily into it it'll be worth to pay the extra to get the full version of the Excel add-on which runs the calculations.

I have this book for sometimes for my research area is simulation. Now I need to seriously study the Risk analysis for the ERP implementation for the real-business and I found this book is quite useful. Comparing to the Risk management book, this book gives you more quantitative analysis using simulation approach. Hence, this book will guide you for more tangible subject rather than intangible subject.
I agree with other reviewer for the "student version" of the software, come with this book. It would be great if the authors provide the "bigger" size than student level for better analysis the bigger system :) I wish the authors read my note here :)

I used this book for a graduate school class and wasn't impressed. The book is poorly written and the questions at the ends of the chapters have too many errors. Learning to use simulation modeling requires hands-on activities and when those activities are poorly conceived, or simply incorrect, it seriously inhibits the learning process. Personally, I'd rather have typos in the main text of the book than unaswerable questions at the end of chapters.

However, the book does provide an introduction to a broad range of areas in which simulation modeling can be used and for lack of a better alternative, I won't give it a lower score. Most of the problems I found with this book could be fixed by using a better editor.

Because I am a big fan of previous works from one of the authors, it came as surprise to me how disappointing this new book is. The book contains a lot of material and discussions that are irrelevant to the science of Fluid Mechanics. In many occasions, the clarity of the theoretical expositions is overshadow by lateral discussions that cannot be justified on a book of such basic level. The reader is left wondering if there was agreement and understanding among the authors on the content selected for the book. I read the book and got the feeling that the book contains parts that are original and interesting. The problem is that they never coincide: when they are interesting they are not original - when they are original they are not interesting. As a whole the book did not add to my collection.

There is no other introduction to fluid mechanics that even comes close to this book. I hope it becomes the standard introduction to the subject. It is unique, clear, and perceptive. The best think I can say for it is this: A student could spend decades trying reach the level of understanding they would get from reading this elegant book.

This book is unique. It is the most comprehensive treatment of the basic theory of Newtonian and non-Newtonian fluids that I know of.
It contains a lot of interesting material that is not treated in elementary texts and is dispersed in the research literature, and the authors present it in a rigorous but engaging manner. What I value most of the book are the insightful comments and discussions throughout; it gives one a proper perspective and a deepened understanding of the subject. The book is not suitable for a first course in fluid mechanics, but it should be an excellent text for beginning graduate students -- both engineering students who want to learn the mathematics and mathematics students who want to understand the physics.

despite the 5***s given above, this is a very dry book and does not explain any concept. The text is overwhelmed with equations and it keeps dragging into too much of mathmatical derivations. I read the book trying to understand some basic models for short fading but unfortionatley could not get much out of it. It's a kind of a research paper style rather than an academic text. The two stars are given for the completness of the derivations of each model.
My advise is that you read this book if you are looking for a thourough anaylsis and derivationsn of mobile channel models, but not if you want to clearly understand those models.

regards

The book is very well written, and gives you all the information all at one place. The theoretical treatment of the subject, which is my primary interest, is the most complete and accessible of all the books/papers that i've come across.

This is the first book that really makes me understand the concept and simulation techniques of fading channels. The author explains everything clearly, (at least, fills me with what I can't figure out from other books), provides proofs that are usually skipped in other books, and makes all the contexts easy to follow. It is a pleasure to read this book from cover to cover.

I have a strong general background in math, but not in probability and statistics. I use this book for self-study, and I find that it fits that purpose excellently. There are plenty of examples, and problems are adjusted so that they focus more on principles and understanding rather than on grunt-work calculations.

My main objection, and the reason for giving it 4 stars, is English language. I am not a native English speaker, and it's obvious that none of the authors is either. Even worse, I encounter at least one misleading, or hard to understand sentence per chapter (mostly among problems). The book most definitely needs proofreading and language corrections!

This book reads easily because it gives many concrete examples and uses a tutorial approach to teaching. However, you still need to know some math! You don't need a math degree. A good first course in calculus covering derivatives and integrals, including logs and exponentials, and some introductory combinatorics (basic knowledge of sets, permutations and combinations) is enough. Any sophomore or, at the latest, junior majoring in engineering or hard science has the prerequisites.

An understanding of probability is necessary for understanding statistics, so the first half of this book is probability. Without probability, statistics becomes something like "here are some facts, trust me, now here are some formulas, recipes and tables and you will learn when to use each one". For many people this may be enough, especially if they just need to get something done. But if you want to know why hypothesis testing is done the way it is and how it works, buy this book. For example, many statistics books just assume a normal distribution for sampling and the only thing you need to learn is when to use a one-tailed or two-tailed test and which formula to use. This is valid when working with sufficiently large populations or samples. In contrast, the worked example in this book does not use a normal distribution and it walks you through the reasoning and calculation. The reasoning is applicable to any population and distribution. When you change to a normal distribution the principles remain the same, only the formulas change. You learn the principles.

Now to the book's style. This is a tutorial style book that teaches using examples. It doesn't skip many steps and can feel somewhat chatty. It repeats simple calculations along the way so you don't have to page back and find where that number was calculated. This keeps the flow going. Learning by example is actually a good way to learn if you are new to the material. Some however, may not like this style, so read some online first before buying. If you already have probability under your belt and are up on your math then you may find this book slow going. This book is aimed at scientists and engineers, so if you are looking for a rigorous math book with proofs, look elsewhere.

Summary: If you've got the prerequisites then this is a great book for self teaching at a good price. If you are lacking in math and you need to do statistics now, then pick up a "cookbook" statistics book and come back later when you have the math background. If you know your stuff and need a reference, look elsewhere.

I really don't feel qualified to rate this book because it quickly went over my head. The first chapters were an easy read and left me wanting to read more of the subject. After that, however the equations quickly progressed to where you may have to have a degree in mathematics to understand it. Not all the variables in the equations were defined, even in the index. This may be a very good text book if taught by someone knowledgeable in the subject, however I could not understand it by reading the book.

If there's one topic this books is not about, it's the numerical geometry of images
despite its title.

This book should have been titled "Introduction to Geometry of Curves on Surfaces."

The closest the author gets to anything resembling the study of the numerical
geometry of images is the Taylor series expansion of derivatives taught in most
undergraduate calculus courses - and two simple MATLAB program of questionable
value at the end the book.

There are a handful of simple algorithms in the chapter 7 but they only address
fast marching methods applied to two silly boundary value problems. The author
completely ignores the corresponding initial value problem.

I'm giving it 2 stars since the title of the book was completely misleading.
And after reading it, I was left wondering how would someone apply the information
presented in this book to a simple 2 dimensional image since there are absolutely
no examples of any practical value.

A very well-written, interesting and useful book covering a wide range of topics in image processing and computer vision and beyond. A good balance between theory and implementation issues that make the things work. A 100% recommendation to students and specialists in the field.

The book is an extended version of my lecture notes. Including
introduction to variational methods, differential geometry,
level sets and fast marching numerical methods, and geometric active
contours for segmentation with Matlab pseudo code, 3D face recognition,
texture mapping and more applications.

Thanks to the many who bought the book.