``Towards the end of the nineteenth century it became clear to many mathematicians that the Riemann integral (about which one learns in calculus courses) should be replaced by some other type of integral, more general and more flexible, better suited for dealing with limiting processes. Among the attempts made in this direction, the most notable ones were due to Jordan, Borel, W. H. Young, and Lebesgue. It was Lebesgue's construction which turned out to be the most succesful.''* The theory continued to develop until the early 50's when it assumed more or less the form in which we know it today. As some authors phrase it, restricting ourselves to Riemann integrable functions was like working with the rational numbers without acknowledging the existence of irrational numbers. Abstract measure and integration theory is a far-reaching and beautiful piece of mathematics that should be part of the general mathematical culture any graduate student in mathematics or statistics is exposed to. This course is an introduction to Lebesgue Integration and Measure Theory which extends familiar notions of length, volume, integration to more general settings. Mathematical probability is an important part of measure theory, this course should provide an excellent background for an advanced course in probability. It is also fundamental background for advanced courses in Functional Analysis, Differential Equations, Harmonic Analysis.

Topics will include: Measurable sets and functions, measures and measure spaces (in particular Lebesgue measure). Next we will develop and integration theory that generalizes Riemann's Integral, and prove basic convergence theorems (Fatou's Lemma, Monotone Convergence Theorem, Lebesgue Dominated Convergence Theorem). Lebesgue spaces ($L^p$) and basic inequalities will be presented. Different modes of convergence will be introduced and compared. We will discuss decomposition and differentiation of measures, and an excursion into functions of bounded variation and absolutely continuous functions is in order. Product measures will be studied and the celebrated Fubini's theorem (on the interchange of integrals) will be proved. Time permitting we will study Hausdorff measure and fractals.

Texts: The book we will use is Real Analysis by Elias Stein and Rami Shakarchi. Princeton Lectures in Analysis III. Princeton Press 2005. We will also use Terence Tao Introduction to Measure Theory which exists as a book published by the AMS and is also available online as a pdf, and annotations and errata can be found in Tao's blog (which is very interesting) Tao's blog.

We also recommend the text The Elements of Integration and Lebesgue Measure by Robert G. Bartle, Wiley Classics Library, 1995. There are a number of classic books that cover the material we will study and more, and that you might want to explore, Real Analysis: Modern Techniques and their Applications by Gerald Folland, John Wiley and Sons, 1999 (2nd edition); Real Analysis by Halsey Royden, Prentice Hall, 1988 (3rd Edition)**; Real and Complex Analysis by Walter Rudin, McGraw-Hill Science/Engineering/Math, 1987 (3rd revised edition)*; and Measure Theory by J. L. Doob, Springer-Verlag, GTM 144, 1994. The later book is written by a probabilist, it integrates probabilistic concepts in the text, and many examples are taken from probability theory. The above are all excellent references for this course.

Prerequisites: Real Analysis MATH 510 or MATH 401/501 or permission from the instructor.

Americans with Disabilities Act: Qualified students with disabilities needing appropriate academic adjustments should contact me as soon as possible to ensure your needs are met in a timely manner. Handouts are available in alternative accessible formats upon request.

Return to: Department of Mathematics and Statistics, University of New Mexico

Last updated: August 6, 2014