# MA359 Measure Theory

**Lecturer: **Josephine Evans

**Term(s):** Term 1

**Status for Mathematics students:** List A

**Commitment:** 30 hours

**Assessment:** 85% Examination, 15% Assignments

**Formal registration prerequisites: **None

**Assumed knowledge: **

MA244 Analysis III or MA258 Mathematical Analysis III:

- Riemann Integration
- Uniform Convergence
- Uniform Continuity

MA260 Norms, Metrics and Topologies or MA222 Metric Spaces:

- Definition of a topology
- Open and closed sets
- Norms

**Useful background:** Good knowledge of core analysis courses including material about continuity and convergence.

**Synergies: **The following modules go well together with Measure Theory:

- MA3G7 Functional Analysis I
- MA3G8 Functional Analysis II
- MA254 Theory of ODEs
- MA250 Introduction to PDEs
- MA3G1 Theory of PDEs

**Leads to: **The following modules have this module listed as **assumed knowledge** or **useful background:**

- MA3G8 Functional Analysis II
- MA3H2 Markov Processes and Percolation Theory
- MA3D4 Fractal Geometry
- MA3K1 Mathematics of Machine Learning
- MA433 Fourier Analysis
- MA4L2 Statistical Mechanics
- MA4A2 Advanced Partial Differential Equations
- MA427 Ergodic Theory
- MA4F7 Brownian Motion
- MA4J0 Advanced Real Analysis
- MA482 Stochastic Analysis
- MA4L3 Large Deviation Theory
- MA4L9 Variational Analysis and Evolution Equations
- MA4M9 Mathematics of Neuronal Networks

**Content**: The modern notion of measure, developed in the late 19th century, is an extension of the notions of length, area or volume. A *measure* *m* is a law which assigns a number to certain subsets *A* of a given space and is a natural generalization of the following notions: 1) length of an interval, 2) area of a plane figure, 3) volume of a solid, 4) amount of mass contained in a region, 5) probability that an event from *A* occurs, etc.

It originated in the real analysis and is used now in many areas of mathematics like, for instance, geometry, probability theory, dynamical systems, functional analysis, etc.

Given a measure *m*, one can define the *integral* of suitable real valued functions with respect to *m*. Riemann integral is applied to continuous functions or functions with ``few`` points of discontinuity. For measurable functions that can be discontinuous ``almost everywhere'' Riemann integral does not make sense. However it is possible to define more flexible and powerful *Lebesgue's integral* (integral with respect to *Lebesgue's measure*) which is one of the key notions of modern analysis.

The Module will cover the following topics: Definition of a measurable space and -additive measures, Construction of a measure from outer measure, Construction of Lebesgue's measure, Lebesgue-Stieltjes measures, Examples of non-measurable sets, Measurable Functions, Integral with respect to a measure, Lusin's Theorem, Egoroff's Theorem, Fatou's Lemma, Monotone Convergence Theorem, Dominated Convergence Theorem, Product Measures and Fubini's Theorem. Selection of advanced topics such as Radon-Nikodym theorem, covering theorems, differentiability of monotone functions almost everywhere, descriptive definition of the Lebesgue integral, description of Riemann integrable functions, *k*-dimensional measures in *n*-dimensional spaces, divergence theorem, Riesz representation theorem, etc.

**Aims**: To introduce the concepts of *measure* and *integral with respect to a measure,* to show their basic properties, and to provide a basis for further studies in Analysis, Probability, and Dynamical Systems.

**Objectives**:

- To gain understanding of the abstract measure theory and definition and main properties of the integral
- To construct Lebesgue's measure on the real line and in
*n*-dimensional Euclidean space - To explain the basic advanced directions of the theory

**Books**: There is no official textbook for the course. As the main recommended book, I would suggest:

- Cohn, D.L, Measure Theory, Second Edition, Birkhauser (2013). *

The list below contains some of many further books that may be used to complement the lectures.

- Folland, G.b.: Real Analysis, Second Edition, Wiley (1999). *
- Halmos, P. R.: Measure Theory, D. Van Nostrand Company Inc., Princeton, N.J. (1950) (Reprinted by Springer (1974)).
- Kubrusly, C.S: Essentials of Measure Theory, Springer (2015). *
- Loeb, P.A: Real Analysis, Birkhauser (2016). *
- Royden, H. L. and Fitzpatrick, P.M: Real Analysis, Fourth Edition, Macmillan Publishing Company (2010).
- Rudin, W.: Real and Complex Analysis, Third Edition, McGraw-Hill Book Company (1987).

- Stein, E. M. and Shakarchi, R.: Real Analysis - measure theory, integration and Hilbert spaces. (Princeton Lectures in Analysis III) Princeton University Press (2005).

* = E-book available from Warwick Library.