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This text introduces engineering students to probability theory and stochastic processes. Along with thorough mathematical development of the subject, the book presents intuitive explanations of key points in order to give students the insights they need to apply math to practical engineering problems. The first seven chapters contain the core material that is essential to any introductory course. In one-semester undergraduate courses, instructors can select material from the remaining chapters to meet their individual goals. Graduate courses can cover all chapters in one semester.
- Sales Rank: #421226 in Books
- Published on: 2014-01-28
- Original language: English
- Number of items: 1
- Dimensions: 9.20" h x .70" w x 7.20" l, .0 pounds
- Binding: Paperback
- 512 pages
About the Author
Roy Yates received the B.S.E. degree in 1983 from Princeton and the S.M. and Ph.D. degrees in 1986 and 1990 from MIT, all in Electrical Engineering. Since 1990, he has been with the Wireless Information Networks Laboratory (WINLAB) and the ECE department at Rutgers University. Presently, he is an Associate Director of WINLAB and a Professor in the ECE Dept. He is a co-author (with David Goodman) of the text Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers published by John Wiley and Sons. He is a co-recipient (with Christopher Rose and Sennur Ulukus) of the 2003 IEEE Marconi Prize Paper Award in Wireless Communications. His research interests include power control, interference suppression and spectrum regulation for wireless systems.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Dense Book: read thoroughly and it's actually friendly
By freefood
This is a good book despite some of the highly negative reviews. The authors structure the content such that concepts are defined before being applied in a theorem. This book is also awesome because it has a lot of examples that are very similar to the homework problems. It doesn't take much abstraction to learn apply the processes demonstrated in the examples to solving your homework problems. Not only that, many example problems are further extended for use in latter examples, which is really helpful because you become very familiar with the example problem. When I was taking the course I knew where all the example problems in the book were because I referred to them more than I did to anything else. MATLAB examples are a nice touch since it teaches you how to apply your knowledge.
I would like to address the most common complaint:
"Hard to Understand/Not Friendly" - There aren't many complex topics introduced here. Most of the content is purely definition or theorem (proofs given). Perhaps people are mistaking their lack of grasp of the practical significance of topics mentioned with confusion due to poor explanation. This happens a lot when engineering is taught in an abstract way, which is how it's taught more often than not. Note: Authors also don't take many shortcuts in the math; the math should be possible to follow if you focus.
3 of 3 people found the following review helpful.
Excellent textbook, very easy to read for engineers
By Cesar
I'm an engineer and am starting to use stochastic process models for work. I'm not giving it a higher rating because there are no answers for practice problems at the end of each chapter in the book. I usually find these in most basic textbooks at the end of the book at least for selected problems. I tried to contact the publisher to ask for these and they said they couldn't provide them for self study but needed to show them "proof of purchase" of a "multi-copy order". It's hard to know if you're "getting it" when there's no way to check your work.
0 of 0 people found the following review helpful.
Difficultly vague on some concepts
By william boler
This is my second course that deals with probability and so far I have found the book fairly challenging to understand. One thing that I should note is that the authors do not outright define that probability is generally the ratio of events to the sample space. Neither do they define cardinality or certain set algebra concepts. You get the basic fundamentals and are expected to work with it. While many in my class are struggling because they did not know these basic concepts, I have been flying through the initial problems due to a priori. In the later chapters when the author presents information that I do not already know, I am having a hard time trying to understand it. The most discouraging thing about the book is that the authors describe one process to solve a problem, but then use a completely different process in the solution manual. There should be in-depth explanations of when to use different methods, rather than to list the definitions and expect us to understand. I appreciate the work put into the book already, but more work needs to be done.
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