92

I post this question with some personal specifications. I hope it does not overlap with old posted questions.

Recently I strongly feel that I have to review the knowledge of measure theory for the sake of starting my thesis.

I am not totally new with measure theory, since I have taken and past one course at the graduate level. Unfortunately, because the lecturer was not so good at teaching, I followed the course by self-study. Now I feel that all the knowledge has gone after the exam and still don’t have a clear overview on the structure of measure theory. And here come my specified requirements for a reference book.

  1. I wish the book elaborates the proofs, since I will read it on my own again, sadly. And this is the most important criterion for the book.

  2. I wish the book covers most of the topics in measure theory. Although the topic of my thesis is on stochastic integration, I do want to review measure theory at a more general level, which means it could emphasize on both aspects of analysis and probability. If such a condition cannot be achieved, I'd like to more focus on probability.

  3. I wish the book could deal with convergences and uniform integrability carefully, as Chung’s probability book.

My expectation is after thorough reading, I could have strong background to start a thesis on stochastic integration at an analytic level.

Sorry for such a tedious question.

P.S: the textbook I used is Schilling’s book: measures, integrals and martingales. It is a pretty good textbook, but misprints really ruin the fun of reading.

newbie
  • 3,441

16 Answers16

42

Textbook: Real and Complex Analysis by Walter Rudin

Explanation: Chapters 1, 2, 3, 6, 7 and 8 constitute an excellent general treatment of measure theory. Let me elaborate:

Chapter 1: The notions of an abtract measure space and an abstract topological space are introduced and studied in concurrence. This treatment allows the reader to see the close connections between the two subjects that appear both in practice and in theory. Elementary examples and properties of measurable functions and measures are discussed. Furthermore, Lebesgue's monotone convergence theorem, Fatou's lemma, and Lebesgue's dominated convergence theorem are proven in this chapter. Finally, the chapter discusses consequences of these results. The elegance of the treatment allows the reader to quickly become accustomed to the basic theory of measure.

Chapter 2: This chapter delves further into the intimate connection between topological and measure theoretic notions. More specifically, the chapter begins with a treatment of some important results in general topology such as Urysohn's lemma and the construction of partitions of unity. Afterwards, these results are applied to establish the Riesz representation theorem for positive linear functionals. The proof of this result is long but is nonetheless carefully broken into small steps and the reader should find little or no difficulty in understanding each of these steps. The Riesz representation theorem is applied in a particularly elegant manner to the theory of positive Borel measures. Finally, the existence and basic properties of the Lebesgue measure are shown to be a virtually trivial consequence of the Riesz representation theorem. The chapter ends with a nice set of exercises that discusses, in particular, some interesting counterexamples in measure theory.

Chapter 3: The basic theory of $L^p$ spaces ($1\leq p\leq \infty$) is introduced. The chapter begins with an elementary treatment of convex functions. Rudin explains that many elementary inequalities in analysis may be established as easy consequences of the theory of convex functions and evidence is provided for this claim. In particular, Holder's and Minkowski's inequalities are proven. These results culminate in the proof that the $L^p$ spaces are indeed complex vector spaces. The completeness of the $L^p$ spaces and various important density results are also discussed.

Chapter 6: This chapter discusses the theory of complex measures, and in particular, the Radon-Nikodym theorem. Von Neumann's proof of the Radon-Nikodym theorem is presented and various consequences are discussed ranging from the characterization of the dual of the $L^p$ spaces ($1\leq p\leq \infty$) to the Hahn decomposition theorem. These results culminate in the proof of the Riesz representation theorem for bounded linear functions. A knowledge of chapters $4$ and $5$ are necessary in this chapter although they do not strictly cover measure theory. Uniform integrability and the Vitali convergence theorem are treated in the exercises at the end of the chapter.

Chapter 7: The main topic of this chapter is Fubini's theorem. A wealth of nice counterexamples is discussed and an important application is presented: the result that the convolution of two functions in $L^1$ is again in $L^1$. A wonderful feature of this treatment is the generality; the result is established in one of the most general forms possible.

Chapter 8: This chapter treats differentiation of measures and the Hardy-Littlewood maximal function which is an important tool in analysis. A number of applications are presented ranging from a proof of the change of variables theorem in Euclidean $n$-space (in a very general form) to a treatment of functions of bounded variation and absolute continuity. Several results from this chapter are also used later in this book; most notable is the use of the differentiation theorem of measures in the study of of harmonic functions in chapter 11.

Let me summarize with some general comments regarding the book:

Prerequisites: A good knowledge of set-theoretic notions, continuity and compactness suffice for the chapters that I have described. An at least rudimentary knowledge of differentiation and uniform convergence is very helpful at times. One need not be acquianted with the theory of the Riemann integral beforehand although one should at least be acquianted with its computation. In short, a knowledge of chapters 1, 2, 3, 4 and 7 of Rudin's earlier book Principles of Mathematical Analysis is advisable before one reads this textbook.

Exercises: The exercises in this textbook are wonderful. Many of the exercises build an intuition of the theory and applications treated in the text and therefore it is advisable to do as many exercises as possible. However, you should expect to work to solve a few of the exercises. A number of important concepts such as convergence in measure, uniform integrability, points of density, Minkowski's inequality for convolution, inclusions between $L^p$ spaces, Hardy's inequality etc. are treated in the exercises. However, if you are truly stuck you will find that many of these results are either theorems or exercises with detailed hints in other textbooks. (E.g., Folland's Real Analysis.)

Content: I have already described the content in some detail but let me say that the content is about exactly what one needs to study branches of mathematics where measure theory is applied. Of course, this is with the assumption that one at least attempts as many exercises as possible since a number of important results (from probability theory, for example) are treated in the exercises.

Style: The proofs in Rudin are (with possibly minor exceptions) complete. Unlike a number of other mathematics textbooks, Rudin prefers not to leave any parts of proofs to the reader and instead focusses on giving the reader non-trivial exercises as practice at the end of each chapter. The book reads magnificently and the flow of results is excellent; almost all results are stated in context. It is fair to say that the main text of the book lacks examples, which is perhaps one of the only points of complaints by students, but the exercises do contain examples. Finally, the book is rigorous and is completely free of mathematical errors.

I hope this review of Rudin's Real and Complex Analysis is helpful! I have read virtually the entire book (over $4$ months) and I found it to be one of the most enjoyable experiences of my life. It really motivated me to delve deeper into analysis. Perhaps the same will be true for you. I certainly recommend this book with my deepest enthusiasm.

Amitesh Datta
  • 20,779
  • 8
    Excellent review and a good suggestion, of course. There is one thing that bothers me, though: You seem to be downvoting concurring answers or answers you don't like. I observed this several times already. Please don't do that, this is really not nice and not especially helpful either. The suggestions of Carl and especially those by ncmathsadist are also excellent, if not better. Look at their profiles and look at who they are. They know what they are talking about. – t.b. Jun 19 '11 at 09:05
  • 2
    @Theo: That kind of question is hard to vote on. For example, I consider this a very helpful answer (e.g. because of the detail), but I don't think that Rudin is a good recommendation. Should I upvote or downvote? I suppose it would be a downvote if the question was community wiki. – Stefan Jun 19 '11 at 10:20
  • 4
    Dear Theo, I downvoted a couple of answers because I felt that there was no indication as to why the recommendation was good. For example, I think Halmos was recommended above but there was no explanation as to why it is good except that the author is well-regarded. I understand I should have at least explained this (in comment form) and not doing this can be injustice to the author. I apologize for this. However, I did not downvote because they were competing answers. (I also vote based on the content and not on the author.) I agree that I have downvoted answers rather harshly in the past. – Amitesh Datta Jun 19 '11 at 11:16
  • Dear Stefan, you are, of course, free to either upvote or downvote my answer. I certainly do not mind if my answer is downvoted since it gives me an indication of what sorts of answers the community likes and if I should improve the answers I author in the future. – Amitesh Datta Jun 19 '11 at 11:40
  • My point is: If your answer is downvoted, you might get the impression that detailed reviews of books are not appreciated, while in fact the reason is just that people don't like the book you recommend. So I think there should be some convention on what a vote means in case of book recommendations. Maybe someone can point me to a meta question about that. I couldn't find any. – Stefan Jun 19 '11 at 12:01
  • Dear Amitesh: Thank you for providing explanations for your downvotes. I think you understood my point: just imagine yourself at the receiving end of the vote. You'd be wondering: what exactly is wrong with that answer? I agree that it is a good policy to vote on content and not on the author but especially when it comes to book suggestions I think that a certain authority should be valued and respected. I'm also much more in favor of detailed and thought-out explanations and I value your contributions very highly because of that, as I told you several times already. However, sometimes a brief – t.b. Jun 19 '11 at 13:03
  • answer with not much more than a link can already answer the question and I think downvotes should be reserved for answers that are genuinely unhelpful or blatantly wrong. But that's my opinion of course and you can choose to do whatever is of your liking. I'm glad to hear that it has nothing to do with the fact that they were competing answers (I didn't really believe that, in fact). – t.b. Jun 19 '11 at 13:06
  • Dear Theo, I think I agree with you regarding downvoting answers; it is probably better to reserve downvotes only for truly unhelpful answers. I will try my best to keep this in mind in the future and I will try to use my downvotes more sparingly. Thank you very much for telling me this; it always helps to hear a different perspective on the matter and I agree in this case that my downvotes were too harsh. Regards, – Amitesh Datta Jun 19 '11 at 13:39
  • Dear Theo, I think I mentioned that uniform integrablity is treated in the exercises although I did not mention that they were treated in the chapter 6 exercises. I will edit my answer shortly. – Amitesh Datta Jun 19 '11 at 13:40
  • @AmiteshDatta do u have manual solution of this books.? if any source pliz provide me the link – jasmine Aug 22 '20 at 13:13
30

Schilling was my introduction to the subject too. There are a few misprints, but a lot of them are corrected in the errata.

I've found Rudin's Real and Complex Analysis useful as a reference / second text. You could also take a look at Folland's Real Analysis. Terry Tao has notes about the subject on his blog, see here.

One of the most comprehensive books, besides Kallenberg's Foundations of Modern Probability, is probably Bogachev's Measure Theory (2-volumes). Its Table of Contents can be viewed at Springer.

  • 4
    I second Bogachev's Measure Theory, too. The two volumes are more than comprehensive and still very accessible. Also, I very much like his functional analyst's point of view on the subject. – lvb Jun 19 '11 at 13:12
  • 4
    Nothing beats Fremlin's measure theory when it comes to comprehensiveness. However, the writing style is idiosyncratic and takes some time of getting used to. – t.b. Jun 19 '11 at 13:28
  • 1
    +1 I like the suggestion of Folland's "Real Analysis". It is undoubtedly a very well written book and has a nice introduction to further topics in analysis at the end. – Amitesh Datta Jun 19 '11 at 13:51
  • @wildildildlife I have heard a number of people express the view that Rudin's Real and Complex Analysis is better as a second text. However, I personally found that after reading Rudin, many of the standard treatments of analysis were actually quite simple to me and I could often point out that: "this is a special case of some result/discussion in Rudin". I have observed that Rudin is more brisk compared to other treatments but I think this can be a good thing; for example (although this is not logically relevant to this question) Rudin covers most of the basic foundations of ... – Amitesh Datta Jun 20 '11 at 05:41
  • ... complex analysis in less than 100 pages. In theory, therefore, one could learn basic complex analysis in a little more than a couple of weeks which is nice. Moreover, once one has done this, one can read other treatments of complex analysis with little or no difficulty. In summary, in the first 300 (short) pages of Rudin, one can go from a rudimentary understanding of set-theoretic notions to a mastery of some of the most important basic notions in modern analysis. (Of course, I hasten to emphasize "basic"; functional analysis, Fourier analysis etc. are only briefly studied.) – Amitesh Datta Jun 20 '11 at 05:44
  • 2
    Sure, if you read an advanced textbook, then less advanced books will seem easy and have less general statements. If you can handle such a book as introduction, all the better. But I suspect that Rudin's treatment of measure would have been slightly over my head when I was learning this stuff. By the way, personally I am not that fond of his treatment of complex analysis. But that's just because I prefer a more geometric/topological spirit, where obviously his spirit is analytical. – wildildildlife Jun 20 '11 at 12:48
  • 1
    +2 for Bogachev and Folland. Folland is a classic that not only covers the material in sufficient breadth, the historical notes are terrific and provide a substantial context. Bogachev is the most comprehensive source that currently exists,is beautifully written and has complete references. My one gripe is it's probably too big to effectively be used as a textbook. – Mathemagician1234 Oct 11 '11 at 03:12
  • @t.b. I really want to read Fremlin now—you’re the second person to recommend his Measure Theory—but I can’t get his .tex files to work for shit on my laptop – gen-ℤ ready to perish May 07 '20 at 18:15
12

Donald L. Cohn-"Measure theory". Everything is detailed.

user10676
  • 8,521
12

Folland's text ("Real Analysis") is highly extensive and covers many topics in measure theory which you rarely see in other books, e.g. interpolation theorems for $L^p$ spaces. It also has a chapter on probability theory, in which he gives rigorous proofs to the basic theorems in the theory (the law of large numbers, the central limit theorem), talks about the construction of product spaces in the context of probability theory, and discusses Brownian motion and Wiener measure.

Mark
  • 5,824
10

When I learned the subject, I found three books to be immensely useful. Royden's Real Analysis is a good general book and has nice problems. Bartle's elements of integration does the abstract theory of integration cleanly and concisely. In addition, you need a good book on Lebesgue measure on Euclidean spaaces. For this I recommend Wheeden and Zygmund's Measure and Integral.

t.b.
  • 78,116
ncmathsadist
  • 49,383
  • 3
    Royden's Real Analysis is a decent book but there are a few features that I did not like. For example, Royden discusses measure theory on Euclidean space and then covers abstract measure theory later in the book. General measure theory provides a more unified approach to the subject and measure theory on Euclidean space is not really simpler; in fact, it brings into the picture many structures on Euclidean space that are not logically relevant and only obscure the general theory ... – Amitesh Datta Jun 19 '11 at 11:29
  • 2
    ... In my opinion, it is much easier and saves the student's time to establish measure theory on Euclidean space as a special case of general measure theory. – Amitesh Datta Jun 19 '11 at 11:29
  • 3
    Actually for a lot of people it is the right level of abstraction. I think it depends on your inclinations. – ncmathsadist Jul 19 '13 at 03:00
  • +1 for Measure and Integral: An Introduction to Real Analysis by wheeden – Bhaskar Vashishth May 02 '15 at 09:19
5

Perhaps my answers will be idiosyncratic, since I'm a philosopher of science who hasn't taken any math classes since Calculus (apart from some logic courses), and my main interest in measure theory was for the sake of probability theory.

David Williams, Probability with Martingales is great for concepts as well as proofs, but there's a lot that it doesn't cover, I feel.

I've gotten a lot out of J.L. Doob's Measure Theory, which presents some common ideas in ways that are more general and deeper than what one usually finds, I believe. Has a bit of droll humor, now and then, too. I don't understand all of it, but I've gotten a lot out of it.

However, these books do not focus on analysis, but seemed worth mentioning.

(*Avner Friedman, Foundations of Modern Analysis has an old-fashioned approach, as I understand, but I learned a lot from it as well. This was where I started. Munroe's Introduction to Measure and Integration fits well with Friedman's book. I used them together.)

(OK, and now feel free to ignore this. Most of the other answers are probably by people who are better informed.)

Mars
  • 1,338
  • 1
    If you've read these books, then it appears you have taken some math since calculus. – David Schneider-Joseph May 22 '20 at 00:47
  • 3
    @DavidSchneider-Joseph My grad school philosophy department's intermediate logic course helped me get comfortable with real proofs. The rest was due to slow self-study with carefully chosen sources, and more persistence and patience than ability. – Mars May 22 '20 at 14:06
  • 4
    Persistently, patiently studying a math book is "taking some math" in my book. :) – David Schneider-Joseph May 23 '20 at 08:46
  • 2
    OK, @DavidSchneider-Joseph. I have revised the incorrect information. :-) – Mars May 25 '20 at 15:13
5

It seems unnecessary to add to this long list of great books, but Real Analysis and Probability by R.M. Dudley is wonderful. His book fits your need to emphasize on both aspects of analysis and probability.

4

If you are looking for a book in measure theory, you should certainly get a copy of the book of that title by Halmos. You may need a second book for details on stochastic processes, but for the underlying analysis it will be hard to find a more comprehensive book, or a better-regarded author.

Carl Mummert
  • 81,604
  • 1
    I agree that Halmos's book on measure theory is an excellent textbook (the same can be said for all of Halmos' publications) but it would help if you detailed somewhat why it is a good book. – Amitesh Datta Jun 19 '11 at 11:32
  • 1
    There is a detailed review and description of the book's contents in MathSciNet. – Carl Mummert Jun 19 '11 at 12:24
  • Halmos' book is classic! But it seems to be a little old-fashioned. Personally I prefer algebra approach rather than rings/monotone class,etc. – newbie Jun 19 '11 at 16:29
  • I like the fact that he has a chapter on probability, but it sounds like the OP already knows what is contained therein. – Trurl Jun 03 '14 at 01:04
  • Halmos' book is classic! But a little old-fashioned! Don't use it as your first textbook. I would prefer Donald Cohn's Measure Theory. – C. Davide Nov 14 '21 at 09:07
3

Lang's Real and Functional Analysis

In my opinion, his treatment of integration is the best one I have ever seen. Usually an author of a textbook on measure theory defines first $\int f d\mu$ for non-negative extended real valued functions $f$(see Rudin for example). Then he defines $\int f d\mu$ for extended real valued functions. And then he defines it for complex valued functions. On the other hand, Lang defines it for real or complex valued functions all at once. And his method also applies without any modification to functions taking values in a Banach space. Most of all, his method is simple, clear and natural. Halmos uses a similar method, but I think Lang is simpler, clearer and more natural.

JHW
  • 393
  • 2
  • 11
3

If you want a book to be a comprehensive study of measure theory, you can hardly be more extensive than the five volumes by Fremlin.

user80034
  • 109
3

[1] Paul Halmos. Measure Theory.

[2] Walter Rudin. Real and Complex Analysis.

[3] Gerald Follamd. Real Analysis.

[4] David Fremlin. Measure Theory. Volume 1-5.

[5] Vladimir Bogachev. Measure Theory. I and II.

[6] Richard Bass. Real Analysis for Graduate Students.

[7] Donald Cohn. Measure Theory.

3

Nobody seems to mention the book "Measure and Integration" by De Barra. It covers all the standard topics and is very detailed. The exercises have detailed solutions too.

Vishal Gupta
  • 6,946
3

Foundations of modern probability, Second edition, by Olav Kallenberg‏.

Shai Covo
  • 24,077
  • I'm voting this up because it's a great book! –  Jun 19 '11 at 16:08
  • Yes, it could be called a bible for probabilists, but I would rather categorize it into probability rather than measure theory, which is well explained by its own title. – newbie Jun 19 '11 at 16:26
  • @newbie Yes, you are quite right. –  Jun 19 '11 at 16:43
2

I found the book Measure, Integration & Real Analysis by Sheldon Axler an excellent read.

  • The book motivates definitions with examples and counterexamples.
  • The book highlights the important parts so that you can peruse it.
  • There are good end-of-chapter problems that drive in the concept learnt.

Axler brings a similar style to his more famous Linear Algebra book to the area of Measure Theory. The book is an easy read while being rigorous.

The book is aimed at active learning and may not be a comprehensive reference book (as requested in the question). Having said that, it provides excellent proofs, motivates definitions well and has a good topic coverage. It also helps that the author has made the book available on open access.

Rahul Madhavan
  • 2,789
  • 1
  • 11
  • 14
1

Richard F. Bass Real Analysis for Graduate Students_ Measure and Integration Theory 2011.this book is very good book for measure theory.this book has very good exercise.

0

I suggest the following books:

The classical books

1- S. J. Taylor, Introduction to Measure and Integration, Cambridge University Press, 1973. (Very constructive book -may be hard-, personally, I use this book for abstract measure theory as a textbook)

2- E. Asplund and L. Bungart, A First Course in Integration, Holt, Rinehart and Winston, Inc., 1966. (It is very useful to understand Lebesgue's Integral and Measure theory on reals.)

Modern Books:

3- F. Burk, Lebesgue Measure and Integration: An Introduction, John Wiley & Sons, Inc. 1998. (my second recommended textbook)

4- F. Jones, Lebesgue Integration on Euclidean, Jones and Bartlett Publishers, 2001.

5- G. S. Nelson, A User-Friendly Introduction to Lebesgue Measure and Integration, AMS, 2015. (Easy to understand, excellent for beginners, Recommended.)

6- W. Johnston, The Lebesgue Integral for Undergraduates, MAA Press 2nd ed 2015. (Easy to understand, excellent for beginners, Recommended.)

7- J. Yeh, PROBLEMS AND PROOFS IN REAL ANALYSIS: Theory of Measure and Integration, World Scientific 2014. (Good collection of exercises but you need to learn first)

General and interesting books are:

1- F. Burk, A Garden of Integrals, MAA, 2007. (I like this book and I recommend reading this book at first)

2- D. M. Bressoud, A Radical Approach to Lebesgue's Theory of Integration, Cambridge University Press, 2008.