Customer Reviews

Introduction to Probability Models, Seventh Edition

5 star:		(2)
4 star:		(2)
3 star:		(1)
2 star:		(1)
1 star:		(0)

 

 

The first four chapters alone (intro, random variables, conditonal probability, markov chains) are worth the price of the book. The author packs each chapter with very interesting examples and problems. The one I found most interesting was his probabilistic analysis of the 2-SAT and SAT problems of computer science. Here he gives an informal math argument as to why 2-SAT is polynomial time decidable and why SAT should be intractable.

On the other hand, I think someone relatively new to probability theory may find his neat problems and examples a bit too much with a first reading. The book is in its seventh edition, and I think Ross has taken advantage of this by providing newer insights and more interesting problems, but in doing so it may overwhelm the novice.

If you are learning probability for the first or second time, I recommend you supplement this book with Roussas "A Course in Mathematical Statistics". Despite its title, the first 9 chapters give a calculus-based intro to probability. And the rest of the book is *excellent* for a calculus-based intro to statistics.

This text really tests your ability to follow logic, summations and algebraic manipulation. Anyone without a sound knowledge of calculus may find some of the problems quite difficult. But the applications to many fields makes this book quite useful and worth the effort.

Especially for the students of industrial engineering may use this book after taking a course about probability. It is an introduction to stochastic processing book and really good for the subjects of Markov Chains and Poisson Process. There is a small part in the book about stochastic modelling and simulation. Be careful that the proofs are not very well and the questions are a bit easy but time consuming. Beside those, do not forget: this is not an introduction to probability but probability models; where some probability knowledge is needed.

I just paid [dollar amount] and it took 4 hours to get through 30 pages! I am no math slouch, but my probability skills are pretty weak. And Ross has one paragraph of explanation to 5 pages of examples and laborious proofs.

As mentioned above, I suggest a supplement or simply another book. Filler/Chang are excellent together. Ross may be good for additional material especially for stochastic processes and simulation. But its definately not an "introduction".

Either as a companion for a measure theoretical probability book, or as a textbook itself for applied probability, this book will help the reader to apply stochastic models in different contexts. It has plenty of examples, and works out details, which allows to gradually increase difficulty.
A great company to Ross's Stochastic Processes.

An extremely good basic section covering stocastic theory. There are also many good examples in this text that are fully worked out. Overall, this is an excellent text well worth having in your collection.