Modeling

This book will help you a lot to make good underwater model photos. It goes through: 1.Bringing out the best 2.Equipment selection and maintenance 3.Diving techniques 4.Underwater modelling techniques 5.Interacting with marine life 6.The basic ten (shows in pictures different poses and how to master them) 7.Understanding light and its effect on the model(colours of suits and cooordinating them) 8.Communications(maybe the best part of the book)

As you see it guides you through all you need to know and it is filled with colour photos and black and white photos which illustrates it all very neatly.

A book recomended for the photographer or the underwater model alike.

My husband and I were going on a long scuba diving trip to Truk lagoon, and we wanted to take many photographs, so this book came up in our search. It was only 10$ , so we decided to take the plunge. It was excellent and goes over everything from what a model should wear(For most of our dives I just wore my bikini since the water is so warm in Truk, but it also describes different wetsuit colors and styles and the considerations for choosing the right one), to makeup (eye shadow, mascara, even lipstick - I chose bright red since that also matched my bikini!) ,hand signals for communication betwen the photographer and model, and how to pose underwater. It even describes how to make just the right bubbles. We applied the techniques we learned and our photographs turned out excellent. Since there are many shipwrecks in Truk, knowing how to pose with the artifacts is dificult, but this book helped tremendously. Thanks to the authors for helping us take great photos!

An excellent survey for anyone contemplating doing research in this area. The authors make a special effort to identify the open research problems, what has been done to date and what there is very little of. This book will bridge the gap for anyone with a background in Molecular Biology that wants to build computer models for cellular and genetic activities. It is especially focused on gene regulation, but also covers other modeling areas such as diffusion. In reading this book, you will appreciatge both the good start this field is off to, but also the long way to go before a complete cell can be modeled. A great area to do pioneering work.

For many years, biologists have been accumulating descriptions of biological "parts" with an almost complete lack of a framework for understanding how those parts might really work together. This book represents the first and so far only example I have seen of an effort to describe modeling techniques that are right now being developed to construct such a framework. There are other books on "computational biology", but most of them are focused only on measuring and comparing different strands of molecules -- this book describes how computational techniques are starting to be applied to actually trying to understand how those molecules work together to generate life. On the outside jacket of the book, Bruce Alberts, President of the National Academy of Science, AND the guy whose book on molecular biology I had to buy for a lot of money when I was in college, describes the authors of this book as being "Brave". I would say it is an introduction to a "Brave New World". This has to be where biology is going -- Each of the chapters are written by different people, and as such there is some variation in readability. I also wish that the color illustrations were part of the chapter they refer to instead of being grouped in the middle. But most of the chapters start with enough of an overview to be understandable to anyone with a decent background in biology. And WOW -- biology is going to get much more exciting!! Oh one other thing -- the art on the inside of the jacket is wonderful - especially in contrast to the black cover with its standard diagram of metabolism -- I wonder if there is a message there :-) .

The book does a good job covering basic data mining terminology and concepts, and provides introductory, but still subtantial coverage of the most common data mining methods.

Positives: a trial version of the easy-to-use Excel-based iDA tool is included with the book, which allows the reader to reproduce the examples (very helpful for understanding the text). iDA may also be used to complete many of the well thought out exercises provided at the end of each chapter. Working with the hands-on examples and exercises is an excellent way to learn data mining, and due to this, the book provides unique and excellent value.

Negatives: the order of topics and chapters seems rather disorganized; topics are often (surprisingly) repeated and the overall structure of the book doesn't seem to make sense at times. But if you read each chapter more or less independently, this isn't a serious problem. The iDA tool that comes with the text is a trial, 180-day version, and it is unlikely that the average reader will want to spend $5,000 to purchase a license for the commercial product after the six month trial is up. So you should buy the book knowing ahead of time that after a while, the iDA tool will no longer be available to go back over the examples or exercises.

Using an Excel-based tool to provide a hands-on approach, this book covers diverse areas in data mining from statistical patterns to data warehousing to the semantic web. Easy to read and with many insights.

This book proved to be a valuable tool to propel me to the next level of understanding of computer science. I gave it four stars because the author skips out on a lot of explanative detail, I felt should of been included, but does an excellent overview/summary of ideas and concepts. I only give it four stars because of it's lack of information. This book is not for the beginner. If you're serious, and want to learn something, then read this one.

A book which will show you how to create datastructure and give you a broad understanding of how to create software. I'm a computer science student at Empire State College and the book seems to cover lots of important details on how programming developts from top to bottom overview. Although the book is good, the authors seems to forget to illustrated some of the 'not very easy to understand point' but overall the main ideas are illustrated in the most understandable manner. 5 starts is not enough for this book. If you are a computer science mayor and taking Datastructures and algorithms, this is a most have book.

The book gives a good basic understanding on encryption,and the e-mail system. Great for a person that wants to know how to secure their e-mail.

For beginners and advanced tinkerers alike, this book is very readable for a technical manual. This is one of the best available on this subject, particularly being comprehensive and dealing with email.

The author is a crypto and computer security consultant.

Includes detailed information on:

PGP and PEM encryption programs,
DES and IDEA encryption algorithms,
The RSA and DSA public-key algorithms,
Key management (critical in security), and
Patent and export issues -- who "owns" the programs

I liked this book because it gave me what I needed to know to comfortably pass the 50-634 exam without getting bogged down with too much detail. I used it as a study aid along with my class textbook and selftest software. Good discussion on time synchronization, WTM.NLM, and merging. There are questions at the end of each chapter and at the end of the book that can be used to really test your knowledge. Would be better if these questions were on CD or the publisher's website. I liked the treatment the book gave regarding an IP environment - it enabled me to answer two or three real exam test questions that I otherwise may have missed. I really rate this book at 4 1/2 stars, almost a 5. The Cram Sheet of key facts provided at the front of the book is more of a marketing gimmick than anything else; it's OK for a quick review before you go into the exam but don't rely on it for anything else.

I read the David Clarke 4.11 books then decided to switch to the CNE 5 track. I read this book one time through with no other reference to any CNE 5 book and I aced the exam. This book actually covers more than the Test Objectives. Excellent book.

This is an excellent book covering the latest thinking in credit risk modeling. It could have used more on documentation and structuring risk, so I only gave it four stars, but it is five stars in model review.

For more on product descriptions and structuring risk, I highly recommend Tavakoli's "Credit Derivatives" 2nd Edition.

This is an excellent treatise on the near state-of-the-art in credit risk management. Although the focus is on sell-side risk management, many (if not all) of the techniques described can be used on the buy-side also.

This is the first book that really focusses on the portfolio problem of credit risk - many books have touched on vendor-provided models and their shortcomings but Bluhm et al. take it further into the practitioner's world.

The reader does not need a very strong background in math or physics but some understanding of finance and stochastic calculus would help to get the most out of it.

I recommend to everyone who is either in or thinking of getting into credit risk as a career - enjoy....

Aimed at senior level undergraduates, the first chapter briefly discusses at a high level what mathematical models are, how they¡¯re formulated and rules of thumb as to how to evaluate them. The rest of the book surveys simple to moderately complex models applied to problems taken from a wide variety of disciplines in business, science, and engineering. As a survey course, brevity and breadth take precedent over depth and the examples are watered-down versions of problems taken from a plethora of sources cited throughout the text. However, the problems and models are not too superficial that they don¡¯t retain the essential issues modelers are likely to encounter.

Although the book is intended primarily for college seniors and first year graduate students, ¡°Part I: Elementary Methods¡± requires only first year calculus and basic probability whereas ¡°Part II: More Advanced Methods¡± also requires differential equations. Therefore, the book will appeal to various levels.

The book is rather dated as is evident by its lack of emphasis on numerical methods and no one should expect to be ready for any serious real world modeling as a result of reading this text alone. However, the book does not pretend to be anything more than what it is and the author cautions that it should merely supplement and not substitute mathematics and science coursework. (I would also add that a few courses in numerical methods and computer science would also be the order of the day.)

Although the first chapter outlines a quick four-step process for formulating mathematical models, the author stresses the role of discussion and research behind each high level step. Any attempt to provide detailed cookbook heuristics would be a sham. Professor Bender also makes a good point about addressing the ambiguity associated with complex problems raised by clients. Indeed, two themes that resonate throughout the examples are redefining the problem by clarifying objectives through discussion as well as iteratively refining a model by adding (useful) detail to an initially crude one. If nothing else, iteratively modeling elucidates the subtleties of the problem under discussion.

Success as an applied mathematician for industry thus requires excellent interpersonal skills and the author clearly reflects this sentiment by requiring group discussions for the book¡¯s exercises containing vaguely stated and open-ended problems having multiple answers. He also notes the crucial role the applied mathematician must play in helping a client clarify his/her objectives.

A must read for any aspiring industrial mathematician.

I read this book for the first time when I was in college. It addresses many fundamental and practical questions with tremendous clarity. It particularly stands out in my mind because of its simple and compelling answers to three questions: Why do we need models of reality? What are the constraints on rigorous modelling (trade-offs between precision vs. generality vs.simplicity)? How to evaluate a mathematical model?

This book is the most current text available on the theory of extreme values. The author eloquently provides us with an understanding of the theory and it vast applications. It is intended for researchers students and practitioners. So it provides an in-depth account of the theory with many real world examples. It contains an excellent up-to-date bibliography. Important theorems are presented with their implications but without mathematical proofs. Computations are done in SPlus. The author provides an appendix on computational aspects that tells the reader where to go to download examples and find the SPlus functions that are used.

Topics include classical extreme value theory and models, threshold models, extremes in dependent stationary cases, extremes for some nonstationary stochastic processes, the point process approach, multivariate extremes and some special topics including extremes in spatial processes and the Bayesian approach to extremes (with examples employing MCMC methods).

I recently used the software accompanied to this book kindly made available by the author and was led to know more about this book and the author's other works. I like what I saw and think the author has done a supeb job in explaining the difficult theory in plain language and in the context of data analysis. Thus it is an "action" book instead of the "just theory" as with most other books. The book provides a balanced treatment of different approaches to extreme value analysis. Personally I prefer the generalized Pareto approach, though theoretically the point process approach may be very neat, if it can be realized.
I think extreme value theory in general is an important statistical area, since in practice one may be forced to deal with analyzing extreme events, such as in financial engineering, environmental or climate analysis, or network design. I wholeheartedly recommend this book for anyone who want to learn this area from one of the leading researchers.

This book focus mainly on spreadsheet techniques for quantitative analysis. Though it provides several interesting topics, it maybe organize and add more issues of real world. It should also provide some basic concepts such as the simplex method of linear programming. It should also emphasize on the objectives of studying every quantitative techniques (maybe present as flowchart) before reading each chapter.

I use this book for my "Quantitative Methods for Decision Making" class and there is some involvement of mathematics. But it is just easy to understand and to use. Also the methods used here are really sort of fundamental and important. I recommend this book for those who really hate deep mathematical involvement in management science.