Reviews (2)

The best introduction to probability and measure
The book very nicely develops the basics of measure theory from a probability perspective (e.g. includes Caratheodory extension theorem, Lebesgue-Stieltjes measures, weak convergence and Kolmogorov extension theorem). It then gives a brief introduction to functional analysis and proceeds to probability theory, martingales and concludes with brownian motion and stochastic integration.

All standard results are given and the book is self-contained. It is a concise, yet readable introduction to this area (less concise then Rudin, Williams but more than Billingsly). An excellent feature of this book is that full solutions to some of the exercises are provided at the end. This makes this book ideal for self-study. The only prerequisite for this book is elementary real analysis (say chapters 1-7 of Rudin's principles of mathematical analysis).

There are other excellent books on measure theory (Rudin, Royden), but if you are interested in measure theory from a probabilistic view this is the book to choose.
As far as a probability textbook, it is clearer and more readable than Billingsly, Chung, Williams and Durrett.

Exceptionally Clear
I first used this text in the earlier version, which comprises the first half of the book, in a one-year course in Hilbert Spaces and Lebesgue Measure theory when in the first year of grad school.The material is presented in a clearly written manner and the exposition is some of the clearest mathematical writing I've seen in a subject which is replete with textbooks.

Reviews (9)

daunting, very duanting
This book is one of the tomes of probability theory.The material covered is not for the faint of heart though.The text explains things as do most graduate level math texts, in proofs and theory.

what is a title?
This is a GREAT book.
Unfortunately, I lost mine.
I wanted to buy volume 1, third edition, to replace the lost book but I got volume 2, second edition. Because volume 1 is SO GREAT book, I decided to keep volume 2 as well. How can be sure I ordered the needed one?

A Reference in Probability Theory
Although people often recommend K.L. Chung at our math department as an introduction to probability theory, i think that Feller is just another view of the problem. If you prefer a concise writing style then Chung is better. On the other hand, Feller's books are full of examples so that you cannot go through this book without having an accurate picture of the historical developments of probability theory and its many applications (even if sometimes applications are driving the need for theory...). This is anyway something you must have read if you want to get an intuitive understanding of probability theory.

Isbn: 0471944041
Sales Rank: 1437918
Subjects:  1. Automata    2. Business / Economics / Finance    3. Business/Economics    4. Case studies    5. Data processing    6. Decision making    7. Finance    8. Industrial management    9. Management    10. Systems And Analysis In Management    11. Applications of Computing    12. Business & Management    13. Economics   

Reviews (17)

Not as much as a step forward as the C version
I have dipped into the C version of NR since it came out almost ten years ago.It has been very useful. Having recently got the C++ version I may have had unrealistically high expectations.It is a good book, but I don't judge that it is that much better from the C version.All modern C++ compilers accept C programs so you can still get the formidable benefits of NR without moving to this version.I particularly disliked the size of the header file in the C++ version.It has to be included in all code (or else write your own individual header files) and since it runs to many thousands of characters, surely it was obvious that no one would spend time entering it?A bad decision for the reader and user, but one that definitely encourages the purchase of the downloadable code package.

Other Edition, but same C++hit
Guys, the next time do it object oriented

This book was a good idea twenty years ago.
This book was a good idea 20 years ago. However, if you want to understand numerical algorithms then you're better off using the WWW to find the pages you want OR buying a real Numerical Methods text. (Amazon has them all!)

Editorial Review

Imagine a world in which computers program other computers based on strategies borrowed from biology and natural selection. Genetic Programming: An Introduction explores fascinating possibilities like these in a thriving area of computer-science research. This research-quality book is for anyone who wants to see what genetic programming is and what it can offer the future of computing.

This text begins by situating genetic programming in terms of the history of computing and machine learning. Early sections show the links between Darwinism, molecular biology, and genetic programming. (Genetic programming uses the strategy of natural selection by solving a problem in successive iterations, which produces the "fittest" solution, much like new species evolve in the natural world.)

The authors present a lot of molecular-biology background since it is central to the genetic-programming project. (There are interesting parallels here. Just as our DNA contains inert information, programs developed using genetic algorithms usually contain many "extra" instructions, too--which often leads to bloated, though effective, code in the final product.) Even though this is extremely technical material, the authors do manage to engage the reader in the imaginative leap from Darwin and DNA to computers and the world of genetic programming.

Later chapters define what genetic programming is and what strategies it uses to let computers program themselves. The authors also examine the state of the art of genetic programming and define what problems need to be solved before it can be widely adopted. The amount of research in this section will mostly benefit specialists in the genetic-programming field.