A Course in Probability Theory, Third Edition
Kai Lai Chung | 2000-10-23 00:00:00 | Academic Press | 419 | Mathematics
Since the publication of the first edition of this classic textbook over thirty years ago, tens of thousands of students have used A Course in Probability Theory. New in this edition is an introduction to measure theory that expands the market, as this treatment is more consistent with current courses.
While there are several books on probability, Chung's book is considered a classic, original work in probability theory due to its elite level of sophistication.
This book assumes that you have a certain degree of mathematical maturity, but gives you very thorough proofs of the basic concepts of rigorous probability. There is no hand waving here. You are expected to have followed an introduction to measure theory. Don't expect to go through this book in a term, but when you have finished it you will be able to consider yourself to be able to come up with proofs like a mathematician. In other words it will leave you with solid foundations.
I can not imagine this book being used as an introduction.
When you are finished you should be ready for a book like Foundations of Modern Probability.
This text by Chung was one of the texts that I used when I was taking a graduate course in probability at Stanford in 1975. It is carefully written but challenging. It provides good coverage of the central limit theorem, the law of large numbers and the law of the iterated logarithm. It also covers stable laws very well. The style is one of rigorous mathematics with theorems, and lemmas given with their mathematical proofs.
The book was recently revised. The revised text does not change much but new material on measure and integration that is now commonly included in the first graduate course in probability has been added. In the 1970s at Stanford a course in measure theory was a prerequisite for the course in advanced probability although some student took it concurrently.
If you plan to get this text, the revised edition is probably worth it. If you already have this edition and know your measure theory, it may not be worth it to get the new edition.
There are several nice books in Grad-level Probability Theory. Billingsley's "Probability and Measure" is the richest one, but somehow poor organized and unpleasant printing. Resnick's "Probability Path" serve best for those who has no time to prepare first in measure theory and Lebesque integration but sacrifice some detail in latter part of the book. If you don't have previous Real Analysis training, I would suggest read Resnick first, and then find Billingsley for reference. But if you already good at measure and integration, Kai Lai Chung's "A Course in Probability Theory" still the best textbook teach step by step without losing detail. Chung's style is friendly to self studying like Resnick, but cover more detail in latter part of the book than Resnick. Chung's book is the best companion fot typical one semester course regradless what textbook your teacher choose. In the other words , Resnick helps students significantly in first half of the semester, Chung helps in the whole semester, and Billingsley may offer best effort after you took the Probability Theory course.
"A course in probability theory", written by Kai Lai Chung, has been referred by not only mathematicians but also mathematical economists.This book is written very rigorously, but almost all of the theorems have easy-to-understand proofs. So it is not difficult to follow. Moreover, there are lots of exercises in this book. So I do recommend this book.
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