The content of each section starts with theory overview and then moves straight into detailed examples heavy in math details. I'm usually one that loves lots of examples but these are mostly useless as the problem statements in both the examples and end of the chapters are written like the author made them up at the last minute. Variable presented are undefined, numbers are reversed from intended or vague, too many assumptions are made and steps left out, and even his standardized variable definitions can get confusing as pointed out before (x, X, x', etc). By the way, the solutions are even worse.

All problems and examples are heavy in difficult math concepts. I know the book is on difficult concepts but it feels like the author likes showing off when the concepts can be explained and practiced in better ways.

The course I'm in is heavy on homework from the back of the book and the problems statements and solutions manual is driving me crazy. I'm tired of trying to read the authors mind and apply difficult concepts at the same time. Over all this book hopefully going to be more useful after this course as a reference then it's been as a learning tool.

as some of the reviews here suggest. This book

- is an easy-going introduction to probability for APPLIED scientists and engineers

- has very many solved problems catering to a typical engineer's tastes

- is not mathematically rigorous (as you might have guessed given its applied focus)

- is better than the pretentious Stark/Woods (which, in addition to not being rigorous, is also imprecise and potentially misleading when it presumes to flirt with rigor)

- is a lot less cluttered than Papoulis (which, of course, is rich in interesting tidbits that you won't find elsewhere; mind the errors in Papoulis, though!)

- is a bit weaker than Sheldon Ross's First Course in Probability and Bertsekas/Tsitsiklis which (apart from Drake and Feller [vol. 1]) is the best possible introduction to applied probability and inference

Leon-Garcia isn't really meant for a grad EE course on applied random processes. For that look up Grimmett/Stirzaker, Gallager's Discrete Stochastic Processes, Casella/Berger (for inference theory), Van Trees (for applied inference), Bremaud etc

as some of the reviews here suggest. This book (

- is an easy-going introduction to probability for APPLIED scientists and engineers

- has very many solved problems catering to a typical engineer's tastes

- is not mathematically rigorous (as you might have guessed given its applied focus)

- is better than the pretentious Stark/Woods (which, in addition to not being rigorous, is also imprecise and potentially misleading when it presumes to flirt with rigor)

- is a lot less cluttered than Papoulis (which, of course, is rich in interesting tidbits that you won't find elsewhere; mind the errors in Papoulis, though!)

- is a bit weaker than Sheldon Ross's First Course in Probability and Bertsekas/Tsitsiklis which (apart from Drake and Feller [vol. 1]) is the best possible introduction to applied probability and inference

Leon-Garcia isn't really meant for a grad EE course on applied random processes. For that look up Grimmett/Stirzaker, Gallager's Discrete Stochastic Processes, Van Trees (for applied inference), Bremaud etc

as some of the reviews here suggest. This book (speaking of the 2nd edition since I haven't seen the 3rd yet)

- is an easy-going introduction to probability for APPLIED scientists and engineers

- has very many solved problems catering to a typical engineer's tastes

- is not mathematically rigorous (as you might have guessed given its applied focus)

- is not very conceptually motivating insofar as discrete and continuous probability spaces are introduced almost concurrently

- is better than the pretentious Stark/Woods (which, in addition to not being rigorous, is also imprecise and potentially misleading when it presumes to flirt with rigor)

- is a lot less cluttered than Papoulis (which, of course, is rich in interesting tidbits that you won't find elsewhere; mind the errors in Papoulis, though!)

- is a bit weaker than Sheldon Ross's First Course in Probability

- is quite a bit weaker than Bertsekas/Tsitsiklis which (together with Drake's classic introduction and Feller [vol. 1]) is the best possible introduction to applied probability and inference

Don't complain about Leon-Garcia being inadequate for a grad EE random processes course - it wasn't meant for that! Look up Grimmett/Stirzaker, Gallager's Discrete Stochastic Processes and Van Trees (for applied inference).

This is a good book for a subject that is simple but not easy. The author introduces the reader to fundamental concepts in probability with minimal mathematical abstraction and more examples than competing texts. This is the best book on the subject I have come across with the exception of the Schaum's Outline for probability and statistics.

The book is a solution set for "Probability and Random Processes for Electrical Engineering". It has some solutions, but not all. I would say about 30% of the problems' answers are given, which is good; but it would be better if they can reduce the font size and include more answers.