The Borel-Cantelli Group

Size: px
Start display at page:

Download "The Borel-Cantelli Group"

Transcription

1 The Borel-Cantelli Group Dorothy Baumer Rong Li Glenn Stark November 14, Borel-Cantelli Lemma Exercise 16 is the introduction of the Borel-Cantelli Lemma using Lebesue measure. An approach using probability measure will be introduced later in the course. Definition 1.1. Let {E 1, E, E 3,...} be a seuence of sets. From this, we can define a decreasing seuence of sets B 1 B B 3... as follows: Let E = {x: x E k for infinitely many k} be defined to mean E = E k E k := lim sup(e n ) n Lemma 1.1. Suppose {E k } k=1 is a countable family of measurable subsets of R d that m(e k ) < k=1 Then E is measurable m(e) = 0. E = lim sup(e k ). k Proof. To show that E is measurable, note that E k. 1

2 Each set B i is the countable union of measurable sets is therefore measurable. Furthermore, E = E k = B n. The set E is the countable intersection of measurable sets is therefore measureable. It remains to be shown that m(e) = 0. Note that Define the two seuences m(e k ) = M <. k=1 n 1 s n = m(e k ) t n = k=1 m(e k ). Note that m(e k ) 0 for all k. Therefore {s n } is a monotone increasing seuence {t n } is a monotone decreasing seuence with the following properties: lim n s n = M the tail lim n t n = 0. In other words, ɛ > 0, N such that n > N t n < ɛ By the properties of measurability, E = ( m(e) = m B n B n ) m(b n ) n N E k

3 Exercise 17 ( m(b n ) = m E k ) m(e k ) = t n. 0 m(e) m(b n ) t n ɛ m(e) = 0 From Stein Sakarchi we have: Let {f n } be a seuence of measurable functions on [0, 1] with < a.e. x. Then a seuence of positive real numbers such that: 0 a.e. x Proof. First we show that k > 0 where for the set A n k = {x: > k} then m (A n k ) < ɛ Assume otherwise, then for some fixed n, ɛ > 0 such that k > 0, then when we let k, we have m {x: f (x) > k} ɛ lim k m {x: > k} ɛ, which forms a decreasing seuence. We define the seuence as follows A = {x: = } A k+1 = {x: > k + 1} A k = {x: > k} The seuence is then decreasing so that A... A k+1 A k.... Then m (A 1 ) m (A ) m (A 3 )... further define lim m (A n) = M > ɛ > 0. n 3

4 Then by Corollary 3.3 from our text which states: for a decreasing seuence where E k decreases to E the m (E k ) < for some k, then m (E) = lim N m (E N) We know this applies to our seuence because we are given that is a countable family of measurable functions x [0, 1]. Furthermore, since A k A by the measurability of each A k by the boundednes of the measure of the seuence, that is, m(a k ) <, we can apply Corollary 3.3 which gives us: lim k m (A k) = m (A k ) When we combine these results with what we were given, by the definition of almost everywhere in our text; < a.e. x, implies m (A ) = m {x: > } = 0 this gives us a contradiction. Our premise was that the measure of the set had to be ɛ for ɛ > 0, however, we have shown that: m {x: > k} < ɛ Note: m (E n ) ɛ m ( n E n) ɛ We can now define ɛ = 1 n let k = cn n. this gives us a seuence of sets, {E n} which we have shown to be a countable family of sets where: E n = { x: > 1 } n In addition, we know the m ( E n ) 1 as constructed for each of the seuences, which implies n (E n) 1. We can then define the set of E as follows { } E = lim sup E n. E = { x: n > 1 } n for i.o. n It is left to show that x appears in infinitely many E n, then by the Borel-Cantelli Lemma, m (E) = 0. If this is true, then fn(x) 0 a.e. x 4

5 Proof. Given some x, we define convergence of the seuence to be lim n 0 ɛ > 0 N > 0 s.t. n > N, < ɛ, If we assume this is not true, this seuence does not converge to zero then we need to show that ɛ > 0, s.t. N > 0 n N > N s.t. f nn (x) N > ɛ 1 If this is true then we know there is an N 1 s.t. N 1 < ɛ n N1 > N where f nn1 (x) > 1 x E nn1 n N1 We can continue in this way show that N s.t. N > n N1 further n N > N > N 1. Now if we let n N1 = N we can continue to find n Nk = N k+1 for infinitely many k. Then by the Borel Cantelli Lemma since this occurs infinitly often since from part one m (E n ) < then we can say: 0 a.e. x 3 Problem 1 Prove that the set of those x R such that there exist infinitely many fractions p/ with relatively prime integers p such that x p 1 +ɛ is a set of measure zero. Proof. For any integers p, define the interval E p It follows that m ( ) E p = +ɛ x p 1 +ɛ x E p. For every integer n N, define A p form p where p [n, n + 1]. Each A p = [ p + 1 +ɛ, p 1 +ɛ ]. to be the set of rational numbers of the 5 is a subset of Q is therefore

6 countable. For each integer, there are exactly + 1 elements of A n of the form p. For example, the set A 0 is the set A 0 = { 0 1, 1 1, 0, 1,, 0 3, 1 3, 3, 3 } 3,.... Conseuently, {E kn } kn An is a countable family of measurable subsets in R m(e kn ) < ( + 1). +ɛ k n A n =1 for infinitely many p A } n Define the sets E (n) = { x [n, n + 1] : x E p E = { x R : x E p for infinitely many p Q}. By the Borel-Cantelli Lemma, for any integer n, the set E (n) is measurable m(e (n) ) = 0. In other words, the set of those x [n, n + 1] such that there exist infinitely many fractions p/ [n, n + 1] with relatively prime integers p such that x p 1 +ɛ is a set of measure zero. Clearly, n=0 E (n) E. To prove E = n=0 E (n), it suffices to show that E n=0 E (n). Assume x R such that x E but x / n=0 E (n). There must exist some integer n such that x [n, n + 1]. In addition, x must also lie in infinitely many intervals E p, each of which must satisfy the reuirement that n 1 < p < n +. It follows that x ( ) E (n 1) E (n) E (n+1) n=0 E (n), which is a contradiction. This proves that which is the desired result. E = m(e ) = n=0 E (n) m(e (n) ) = 0 6

Problem set 1, Real Analysis I, Spring, 2015.

Problem set 1, Real Analysis I, Spring, 2015. Problem set 1, Real Analysis I, Spring, 015. (1) Let f n : D R be a sequence of functions with domain D R n. Recall that f n f uniformly if and only if for all ɛ > 0, there is an N = N(ɛ) so that if n

More information

Existence of a Limit on a Dense Set, and. Construction of Continuous Functions on Special Sets

Existence of a Limit on a Dense Set, and. Construction of Continuous Functions on Special Sets Existence of a Limit on a Dense Set, and Construction of Continuous Functions on Special Sets REU 2012 Recap: Definitions Definition Given a real-valued function f, the limit of f exists at a point c R

More information

Homework 11. Solutions

Homework 11. Solutions Homework 11. Solutions Problem 2.3.2. Let f n : R R be 1/n times the characteristic function of the interval (0, n). Show that f n 0 uniformly and f n µ L = 1. Why isn t it a counterexample to the Lebesgue

More information

HW 4 SOLUTIONS. , x + x x 1 ) 2

HW 4 SOLUTIONS. , x + x x 1 ) 2 HW 4 SOLUTIONS The Way of Analysis p. 98: 1.) Suppose that A is open. Show that A minus a finite set is still open. This follows by induction as long as A minus one point x is still open. To see that A

More information

ABSTRACT INTEGRATION CHAPTER ONE

ABSTRACT INTEGRATION CHAPTER ONE CHAPTER ONE ABSTRACT INTEGRATION Version 1.1 No rights reserved. Any part of this work can be reproduced or transmitted in any form or by any means. Suggestions and errors are invited and can be mailed

More information

Math 104: Homework 7 solutions

Math 104: Homework 7 solutions Math 04: Homework 7 solutions. (a) The derivative of f () = is f () = 2 which is unbounded as 0. Since f () is continuous on [0, ], it is uniformly continous on this interval by Theorem 9.2. Hence for

More information

From now on, we will represent a metric space with (X, d). Here are some examples: i=1 (x i y i ) p ) 1 p, p 1.

From now on, we will represent a metric space with (X, d). Here are some examples: i=1 (x i y i ) p ) 1 p, p 1. Chapter 1 Metric spaces 1.1 Metric and convergence We will begin with some basic concepts. Definition 1.1. (Metric space) Metric space is a set X, with a metric satisfying: 1. d(x, y) 0, d(x, y) = 0 x

More information

Problem Set 2: Solutions Math 201A: Fall 2016

Problem Set 2: Solutions Math 201A: Fall 2016 Problem Set 2: s Math 201A: Fall 2016 Problem 1. (a) Prove that a closed subset of a complete metric space is complete. (b) Prove that a closed subset of a compact metric space is compact. (c) Prove that

More information

Real Analysis Chapter 1 Solutions Jonathan Conder

Real Analysis Chapter 1 Solutions Jonathan Conder 3. (a) Let M be an infinite σ-algebra of subsets of some set X. There exists a countably infinite subcollection C M, and we may choose C to be closed under taking complements (adding in missing complements

More information

MAT1000 ASSIGNMENT 1. a k 3 k. x =

MAT1000 ASSIGNMENT 1. a k 3 k. x = MAT1000 ASSIGNMENT 1 VITALY KUZNETSOV Question 1 (Exercise 2 on page 37). Tne Cantor set C can also be described in terms of ternary expansions. (a) Every number in [0, 1] has a ternary expansion x = a

More information

Lecture 3: Probability Measures - 2

Lecture 3: Probability Measures - 2 Lecture 3: Probability Measures - 2 1. Continuation of measures 1.1 Problem of continuation of a probability measure 1.2 Outer measure 1.3 Lebesgue outer measure 1.4 Lebesgue continuation of an elementary

More information

Sequences. Chapter 3. n + 1 3n + 2 sin n n. 3. lim (ln(n + 1) ln n) 1. lim. 2. lim. 4. lim (1 + n)1/n. Answers: 1. 1/3; 2. 0; 3. 0; 4. 1.

Sequences. Chapter 3. n + 1 3n + 2 sin n n. 3. lim (ln(n + 1) ln n) 1. lim. 2. lim. 4. lim (1 + n)1/n. Answers: 1. 1/3; 2. 0; 3. 0; 4. 1. Chapter 3 Sequences Both the main elements of calculus (differentiation and integration) require the notion of a limit. Sequences will play a central role when we work with limits. Definition 3.. A Sequence

More information

Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall.

Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall. .1 Limits of Sequences. CHAPTER.1.0. a) True. If converges, then there is an M > 0 such that M. Choose by Archimedes an N N such that N > M/ε. Then n N implies /n M/n M/N < ε. b) False. = n does not converge,

More information

Lecture 11: Random Variables

Lecture 11: Random Variables EE5110: Probability Foundations for Electrical Engineers July-November 2015 Lecture 11: Random Variables Lecturer: Dr. Krishna Jagannathan Scribe: Sudharsan, Gopal, Arjun B, Debayani The study of random

More information

3 (Due ). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure?

3 (Due ). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure? MA 645-4A (Real Analysis), Dr. Chernov Homework assignment 1 (Due ). Show that the open disk x 2 + y 2 < 1 is a countable union of planar elementary sets. Show that the closed disk x 2 + y 2 1 is a countable

More information

M17 MAT25-21 HOMEWORK 6

M17 MAT25-21 HOMEWORK 6 M17 MAT25-21 HOMEWORK 6 DUE 10:00AM WEDNESDAY SEPTEMBER 13TH 1. To Hand In Double Series. The exercises in this section will guide you to complete the proof of the following theorem: Theorem 1: Absolute

More information

Defining the Integral

Defining the Integral Defining the Integral In these notes we provide a careful definition of the Lebesgue integral and we prove each of the three main convergence theorems. For the duration of these notes, let (, M, µ) be

More information

Math212a1411 Lebesgue measure.

Math212a1411 Lebesgue measure. Math212a1411 Lebesgue measure. October 14, 2014 Reminder No class this Thursday Today s lecture will be devoted to Lebesgue measure, a creation of Henri Lebesgue, in his thesis, one of the most famous

More information

Probability and Measure

Probability and Measure Probability and Measure Robert L. Wolpert Institute of Statistics and Decision Sciences Duke University, Durham, NC, USA Convergence of Random Variables 1. Convergence Concepts 1.1. Convergence of Real

More information

MT804 Analysis Homework II

MT804 Analysis Homework II MT804 Analysis Homework II Eudoxus October 6, 2008 p. 135 4.5.1, 4.5.2 p. 136 4.5.3 part a only) p. 140 4.6.1 Exercise 4.5.1 Use the Intermediate Value Theorem to prove that every polynomial of with real

More information

Week 2: Sequences and Series

Week 2: Sequences and Series QF0: Quantitative Finance August 29, 207 Week 2: Sequences and Series Facilitator: Christopher Ting AY 207/208 Mathematicians have tried in vain to this day to discover some order in the sequence of prime

More information

Austin Mohr Math 704 Homework 6

Austin Mohr Math 704 Homework 6 Austin Mohr Math 704 Homework 6 Problem 1 Integrability of f on R does not necessarily imply the convergence of f(x) to 0 as x. a. There exists a positive continuous function f on R so that f is integrable

More information

1 Lecture 4: Set topology on metric spaces, 8/17/2012

1 Lecture 4: Set topology on metric spaces, 8/17/2012 Summer Jump-Start Program for Analysis, 01 Song-Ying Li 1 Lecture : Set topology on metric spaces, 8/17/01 Definition 1.1. Let (X, d) be a metric space; E is a subset of X. Then: (i) x E is an interior

More information

Real Analysis Notes. Thomas Goller

Real Analysis Notes. Thomas Goller Real Analysis Notes Thomas Goller September 4, 2011 Contents 1 Abstract Measure Spaces 2 1.1 Basic Definitions........................... 2 1.2 Measurable Functions........................ 2 1.3 Integration..............................

More information

Three hours THE UNIVERSITY OF MANCHESTER. 24th January

Three hours THE UNIVERSITY OF MANCHESTER. 24th January Three hours MATH41011 THE UNIVERSITY OF MANCHESTER FOURIER ANALYSIS AND LEBESGUE INTEGRATION 24th January 2013 9.45 12.45 Answer ALL SIX questions in Section A (25 marks in total). Answer THREE of the

More information

Indeed, if we want m to be compatible with taking limits, it should be countably additive, meaning that ( )

Indeed, if we want m to be compatible with taking limits, it should be countably additive, meaning that ( ) Lebesgue Measure The idea of the Lebesgue integral is to first define a measure on subsets of R. That is, we wish to assign a number m(s to each subset S of R, representing the total length that S takes

More information

A PECULIAR COIN-TOSSING MODEL

A PECULIAR COIN-TOSSING MODEL A PECULIAR COIN-TOSSING MODEL EDWARD J. GREEN 1. Coin tossing according to de Finetti A coin is drawn at random from a finite set of coins. Each coin generates an i.i.d. sequence of outcomes (heads or

More information

Math LM (24543) Lectures 01

Math LM (24543) Lectures 01 Math 32300 LM (24543) Lectures 01 Ethan Akin Office: NAC 6/287 Phone: 650-5136 Email: ethanakin@earthlink.net Spring, 2018 Contents Introduction, Ross Chapter 1 and Appendix The Natural Numbers N and The

More information

Lebesgue Integration: A non-rigorous introduction. What is wrong with Riemann integration?

Lebesgue Integration: A non-rigorous introduction. What is wrong with Riemann integration? Lebesgue Integration: A non-rigorous introduction What is wrong with Riemann integration? xample. Let f(x) = { 0 for x Q 1 for x / Q. The upper integral is 1, while the lower integral is 0. Yet, the function

More information

Probability and Measure

Probability and Measure Part II Year 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2018 84 Paper 4, Section II 26J Let (X, A) be a measurable space. Let T : X X be a measurable map, and µ a probability

More information

(1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define

(1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define Homework, Real Analysis I, Fall, 2010. (1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define ρ(f, g) = 1 0 f(x) g(x) dx. Show that

More information

In N we can do addition, but in order to do subtraction we need to extend N to the integers

In N we can do addition, but in order to do subtraction we need to extend N to the integers Chapter The Real Numbers.. Some Preliminaries Discussion: The Irrationality of 2. We begin with the natural numbers N = {, 2, 3, }. In N we can do addition, but in order to do subtraction we need to extend

More information

MATH 202B - Problem Set 5

MATH 202B - Problem Set 5 MATH 202B - Problem Set 5 Walid Krichene (23265217) March 6, 2013 (5.1) Show that there exists a continuous function F : [0, 1] R which is monotonic on no interval of positive length. proof We know there

More information

In N we can do addition, but in order to do subtraction we need to extend N to the integers

In N we can do addition, but in order to do subtraction we need to extend N to the integers Chapter 1 The Real Numbers 1.1. Some Preliminaries Discussion: The Irrationality of 2. We begin with the natural numbers N = {1, 2, 3, }. In N we can do addition, but in order to do subtraction we need

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 3 9/10/2008 CONDITIONING AND INDEPENDENCE

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 3 9/10/2008 CONDITIONING AND INDEPENDENCE MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 3 9/10/2008 CONDITIONING AND INDEPENDENCE Most of the material in this lecture is covered in [Bertsekas & Tsitsiklis] Sections 1.3-1.5

More information

Notes for Math 290 using Introduction to Mathematical Proofs by Charles E. Roberts, Jr.

Notes for Math 290 using Introduction to Mathematical Proofs by Charles E. Roberts, Jr. Notes for Math 290 using Introduction to Mathematical Proofs by Charles E. Roberts, Jr. Chapter : Logic Topics:. Statements, Negation, and Compound Statements.2 Truth Tables and Logical Equivalences.3

More information

1 Topology Definition of a topology Basis (Base) of a topology The subspace topology & the product topology on X Y 3

1 Topology Definition of a topology Basis (Base) of a topology The subspace topology & the product topology on X Y 3 Index Page 1 Topology 2 1.1 Definition of a topology 2 1.2 Basis (Base) of a topology 2 1.3 The subspace topology & the product topology on X Y 3 1.4 Basic topology concepts: limit points, closed sets,

More information

Section 2: Classes of Sets

Section 2: Classes of Sets Section 2: Classes of Sets Notation: If A, B are subsets of X, then A \ B denotes the set difference, A \ B = {x A : x B}. A B denotes the symmetric difference. A B = (A \ B) (B \ A) = (A B) \ (A B). Remarks

More information

1. Supremum and Infimum Remark: In this sections, all the subsets of R are assumed to be nonempty.

1. Supremum and Infimum Remark: In this sections, all the subsets of R are assumed to be nonempty. 1. Supremum and Infimum Remark: In this sections, all the subsets of R are assumed to be nonempty. Let E be a subset of R. We say that E is bounded above if there exists a real number U such that x U for

More information

MATH 140B - HW 5 SOLUTIONS

MATH 140B - HW 5 SOLUTIONS MATH 140B - HW 5 SOLUTIONS Problem 1 (WR Ch 7 #8). If I (x) = { 0 (x 0), 1 (x > 0), if {x n } is a sequence of distinct points of (a,b), and if c n converges, prove that the series f (x) = c n I (x x n

More information

2 Measure Theory. 2.1 Measures

2 Measure Theory. 2.1 Measures 2 Measure Theory 2.1 Measures A lot of this exposition is motivated by Folland s wonderful text, Real Analysis: Modern Techniques and Their Applications. Perhaps the most ubiquitous measure in our lives

More information

Math 117: Topology of the Real Numbers

Math 117: Topology of the Real Numbers Math 117: Topology of the Real Numbers John Douglas Moore November 10, 2008 The goal of these notes is to highlight the most important topics presented in Chapter 3 of the text [1] and to provide a few

More information

Spring 2014 Advanced Probability Overview. Lecture Notes Set 1: Course Overview, σ-fields, and Measures

Spring 2014 Advanced Probability Overview. Lecture Notes Set 1: Course Overview, σ-fields, and Measures 36-752 Spring 2014 Advanced Probability Overview Lecture Notes Set 1: Course Overview, σ-fields, and Measures Instructor: Jing Lei Associated reading: Sec 1.1-1.4 of Ash and Doléans-Dade; Sec 1.1 and A.1

More information

1.4 Outer measures 10 CHAPTER 1. MEASURE

1.4 Outer measures 10 CHAPTER 1. MEASURE 10 CHAPTER 1. MEASURE 1.3.6. ( Almost everywhere and null sets If (X, A, µ is a measure space, then a set in A is called a null set (or µ-null if its measure is 0. Clearly a countable union of null sets

More information

Math 117: Infinite Sequences

Math 117: Infinite Sequences Math 7: Infinite Sequences John Douglas Moore November, 008 The three main theorems in the theory of infinite sequences are the Monotone Convergence Theorem, the Cauchy Sequence Theorem and the Subsequence

More information

5 Set Operations, Functions, and Counting

5 Set Operations, Functions, and Counting 5 Set Operations, Functions, and Counting Let N denote the positive integers, N 0 := N {0} be the non-negative integers and Z = N 0 ( N) the positive and negative integers including 0, Q the rational numbers,

More information

Annalee Gomm Math 714: Assignment #2

Annalee Gomm Math 714: Assignment #2 Annalee Gomm Math 714: Assignment #2 3.32. Verify that if A M, λ(a = 0, and B A, then B M and λ(b = 0. Suppose that A M with λ(a = 0, and let B be any subset of A. By the nonnegativity and monotonicity

More information

µ (X) := inf l(i k ) where X k=1 I k, I k an open interval Notice that is a map from subsets of R to non-negative number together with infinity

µ (X) := inf l(i k ) where X k=1 I k, I k an open interval Notice that is a map from subsets of R to non-negative number together with infinity A crash course in Lebesgue measure theory, Math 317, Intro to Analysis II These lecture notes are inspired by the third edition of Royden s Real analysis. The Jordan content is an attempt to extend the

More information

1 Sequences of events and their limits

1 Sequences of events and their limits O.H. Probability II (MATH 2647 M15 1 Sequences of events and their limits 1.1 Monotone sequences of events Sequences of events arise naturally when a probabilistic experiment is repeated many times. For

More information

g 2 (x) (1/3)M 1 = (1/3)(2/3)M.

g 2 (x) (1/3)M 1 = (1/3)(2/3)M. COMPACTNESS If C R n is closed and bounded, then by B-W it is sequentially compact: any sequence of points in C has a subsequence converging to a point in C Conversely, any sequentially compact C R n is

More information

Solutions Manual for Homework Sets Math 401. Dr Vignon S. Oussa

Solutions Manual for Homework Sets Math 401. Dr Vignon S. Oussa 1 Solutions Manual for Homework Sets Math 401 Dr Vignon S. Oussa Solutions Homework Set 0 Math 401 Fall 2015 1. (Direct Proof) Assume that x and y are odd integers. Then there exist integers u and v such

More information

Similar to sequence, note that a series converges if and only if its tail converges, that is, r 1 r ( 1 < r < 1), ( 1) k k. r k =

Similar to sequence, note that a series converges if and only if its tail converges, that is, r 1 r ( 1 < r < 1), ( 1) k k. r k = Infinite Series We say an infinite series a k converges to s if its sequence of initial sums converges to s, that is, lim( n a k : n N) = s. Similar to sequence, note that a series converges if and only

More information

7 About Egorov s and Lusin s theorems

7 About Egorov s and Lusin s theorems Tel Aviv University, 2013 Measure and category 62 7 About Egorov s and Lusin s theorems 7a About Severini-Egorov theorem.......... 62 7b About Lusin s theorem............... 64 7c About measurable functions............

More information

3 Integration and Expectation

3 Integration and Expectation 3 Integration and Expectation 3.1 Construction of the Lebesgue Integral Let (, F, µ) be a measure space (not necessarily a probability space). Our objective will be to define the Lebesgue integral R fdµ

More information

Lebesgue Measure. Dung Le 1

Lebesgue Measure. Dung Le 1 Lebesgue Measure Dung Le 1 1 Introduction How do we measure the size of a set in IR? Let s start with the simplest ones: intervals. Obviously, the natural candidate for a measure of an interval is its

More information

Analysis Finite and Infinite Sets The Real Numbers The Cantor Set

Analysis Finite and Infinite Sets The Real Numbers The Cantor Set Analysis Finite and Infinite Sets Definition. An initial segment is {n N n n 0 }. Definition. A finite set can be put into one-to-one correspondence with an initial segment. The empty set is also considered

More information

FUNDAMENTALS OF REAL ANALYSIS by. II.1. Prelude. Recall that the Riemann integral of a real-valued function f on an interval [a, b] is defined as

FUNDAMENTALS OF REAL ANALYSIS by. II.1. Prelude. Recall that the Riemann integral of a real-valued function f on an interval [a, b] is defined as FUNDAMENTALS OF REAL ANALYSIS by Doğan Çömez II. MEASURES AND MEASURE SPACES II.1. Prelude Recall that the Riemann integral of a real-valued function f on an interval [a, b] is defined as b n f(xdx :=

More information

MATH5011 Real Analysis I. Exercise 1 Suggested Solution

MATH5011 Real Analysis I. Exercise 1 Suggested Solution MATH5011 Real Analysis I Exercise 1 Suggested Solution Notations in the notes are used. (1) Show that every open set in R can be written as a countable union of mutually disjoint open intervals. Hint:

More information

REAL AND COMPLEX ANALYSIS

REAL AND COMPLEX ANALYSIS REAL AND COMPLE ANALYSIS Third Edition Walter Rudin Professor of Mathematics University of Wisconsin, Madison Version 1.1 No rights reserved. Any part of this work can be reproduced or transmitted in any

More information

Math 172 HW 1 Solutions

Math 172 HW 1 Solutions Math 172 HW 1 Solutions Joey Zou April 15, 2017 Problem 1: Prove that the Cantor set C constructed in the text is totally disconnected and perfect. In other words, given two distinct points x, y C, there

More information

FINAL EXAM Math 25 Temple-F06

FINAL EXAM Math 25 Temple-F06 FINAL EXAM Math 25 Temple-F06 Write solutions on the paper provided. Put your name on this exam sheet, and staple it to the front of your finished exam. Do Not Write On This Exam Sheet. Problem 1. (Short

More information

Measurable functions are approximately nice, even if look terrible.

Measurable functions are approximately nice, even if look terrible. Tel Aviv University, 2015 Functions of real variables 74 7 Approximation 7a A terrible integrable function........... 74 7b Approximation of sets................ 76 7c Approximation of functions............

More information

Math212a1413 The Lebesgue integral.

Math212a1413 The Lebesgue integral. Math212a1413 The Lebesgue integral. October 28, 2014 Simple functions. In what follows, (X, F, m) is a space with a σ-field of sets, and m a measure on F. The purpose of today s lecture is to develop the

More information

Metric Spaces Math 413 Honors Project

Metric Spaces Math 413 Honors Project Metric Spaces Math 413 Honors Project 1 Metric Spaces Definition 1.1 Let X be a set. A metric on X is a function d : X X R such that for all x, y, z X: i) d(x, y) = d(y, x); ii) d(x, y) = 0 if and only

More information

Chapter 4. Measure Theory. 1. Measure Spaces

Chapter 4. Measure Theory. 1. Measure Spaces Chapter 4. Measure Theory 1. Measure Spaces Let X be a nonempty set. A collection S of subsets of X is said to be an algebra on X if S has the following properties: 1. X S; 2. if A S, then A c S; 3. if

More information

6.2 Fubini s Theorem. (µ ν)(c) = f C (x) dµ(x). (6.2) Proof. Note that (X Y, A B, µ ν) must be σ-finite as well, so that.

6.2 Fubini s Theorem. (µ ν)(c) = f C (x) dµ(x). (6.2) Proof. Note that (X Y, A B, µ ν) must be σ-finite as well, so that. 6.2 Fubini s Theorem Theorem 6.2.1. (Fubini s theorem - first form) Let (, A, µ) and (, B, ν) be complete σ-finite measure spaces. Let C = A B. Then for each µ ν- measurable set C C the section x C is

More information

Definitions & Theorems

Definitions & Theorems Definitions & Theorems Math 147, Fall 2009 December 19, 2010 Contents 1 Logic 2 1.1 Sets.................................................. 2 1.2 The Peano axioms..........................................

More information

Stat 451: Solutions to Assignment #1

Stat 451: Solutions to Assignment #1 Stat 451: Solutions to Assignment #1 2.1) By definition, 2 Ω is the set of all subsets of Ω. Therefore, to show that 2 Ω is a σ-algebra we must show that the conditions of the definition σ-algebra are

More information

Real Analysis Chapter 3 Solutions Jonathan Conder. ν(f n ) = lim

Real Analysis Chapter 3 Solutions Jonathan Conder. ν(f n ) = lim . Suppose ( n ) n is an increasing sequence in M. For each n N define F n : n \ n (with 0 : ). Clearly ν( n n ) ν( nf n ) ν(f n ) lim n If ( n ) n is a decreasing sequence in M and ν( )

More information

THEOREMS, ETC., FOR MATH 515

THEOREMS, ETC., FOR MATH 515 THEOREMS, ETC., FOR MATH 515 Proposition 1 (=comment on page 17). If A is an algebra, then any finite union or finite intersection of sets in A is also in A. Proposition 2 (=Proposition 1.1). For every

More information

Lebesgue measure and integration

Lebesgue measure and integration Chapter 4 Lebesgue measure and integration If you look back at what you have learned in your earlier mathematics courses, you will definitely recall a lot about area and volume from the simple formulas

More information

Product measures, Tonelli s and Fubini s theorems For use in MAT4410, autumn 2017 Nadia S. Larsen. 17 November 2017.

Product measures, Tonelli s and Fubini s theorems For use in MAT4410, autumn 2017 Nadia S. Larsen. 17 November 2017. Product measures, Tonelli s and Fubini s theorems For use in MAT4410, autumn 017 Nadia S. Larsen 17 November 017. 1. Construction of the product measure The purpose of these notes is to prove the main

More information

Entrance Exam, Real Analysis September 1, 2017 Solve exactly 6 out of the 8 problems

Entrance Exam, Real Analysis September 1, 2017 Solve exactly 6 out of the 8 problems September, 27 Solve exactly 6 out of the 8 problems. Prove by denition (in ɛ δ language) that f(x) = + x 2 is uniformly continuous in (, ). Is f(x) uniformly continuous in (, )? Prove your conclusion.

More information

7 Complete metric spaces and function spaces

7 Complete metric spaces and function spaces 7 Complete metric spaces and function spaces 7.1 Completeness Let (X, d) be a metric space. Definition 7.1. A sequence (x n ) n N in X is a Cauchy sequence if for any ɛ > 0, there is N N such that n, m

More information

Compendium and Solutions to exercises TMA4225 Foundation of analysis

Compendium and Solutions to exercises TMA4225 Foundation of analysis Compendium and Solutions to exercises TMA4225 Foundation of analysis Ruben Spaans December 6, 2010 1 Introduction This compendium contains a lexicon over definitions and exercises with solutions. Throughout

More information

G1CMIN Measure and Integration

G1CMIN Measure and Integration G1CMIN Measure and Integration 2003-4 Prof. J.K. Langley May 13, 2004 1 Introduction Books: W. Rudin, Real and Complex Analysis ; H.L. Royden, Real Analysis (QA331). Lecturer: Prof. J.K. Langley (jkl@maths,

More information

Probability Theory. Richard F. Bass

Probability Theory. Richard F. Bass Probability Theory Richard F. Bass ii c Copyright 2014 Richard F. Bass Contents 1 Basic notions 1 1.1 A few definitions from measure theory............. 1 1.2 Definitions............................. 2

More information

Chapter 1. Measure Spaces. 1.1 Algebras and σ algebras of sets Notation and preliminaries

Chapter 1. Measure Spaces. 1.1 Algebras and σ algebras of sets Notation and preliminaries Chapter 1 Measure Spaces 1.1 Algebras and σ algebras of sets 1.1.1 Notation and preliminaries We shall denote by X a nonempty set, by P(X) the set of all parts (i.e., subsets) of X, and by the empty set.

More information

Logical Connectives and Quantifiers

Logical Connectives and Quantifiers Chapter 1 Logical Connectives and Quantifiers 1.1 Logical Connectives 1.2 Quantifiers 1.3 Techniques of Proof: I 1.4 Techniques of Proof: II Theorem 1. Let f be a continuous function. If 1 f(x)dx 0, then

More information

Quick Tour of the Topology of R. Steven Hurder, Dave Marker, & John Wood 1

Quick Tour of the Topology of R. Steven Hurder, Dave Marker, & John Wood 1 Quick Tour of the Topology of R Steven Hurder, Dave Marker, & John Wood 1 1 Department of Mathematics, University of Illinois at Chicago April 17, 2003 Preface i Chapter 1. The Topology of R 1 1. Open

More information

The Lebesgue Integral

The Lebesgue Integral The Lebesgue Integral Brent Nelson In these notes we give an introduction to the Lebesgue integral, assuming only a knowledge of metric spaces and the iemann integral. For more details see [1, Chapters

More information

The Heine-Borel and Arzela-Ascoli Theorems

The Heine-Borel and Arzela-Ascoli Theorems The Heine-Borel and Arzela-Ascoli Theorems David Jekel October 29, 2016 This paper explains two important results about compactness, the Heine- Borel theorem and the Arzela-Ascoli theorem. We prove them

More information

3 Measurable Functions

3 Measurable Functions 3 Measurable Functions Notation A pair (X, F) where F is a σ-field of subsets of X is a measurable space. If µ is a measure on F then (X, F, µ) is a measure space. If µ(x) < then (X, F, µ) is a probability

More information

Lecture 9. d N(0, 1). Now we fix n and think of a SRW on [0,1]. We take the k th step at time k n. and our increments are ± 1

Lecture 9. d N(0, 1). Now we fix n and think of a SRW on [0,1]. We take the k th step at time k n. and our increments are ± 1 Random Walks and Brownian Motion Tel Aviv University Spring 011 Lecture date: May 0, 011 Lecture 9 Instructor: Ron Peled Scribe: Jonathan Hermon In today s lecture we present the Brownian motion (BM).

More information

Math 141: Lecture 19

Math 141: Lecture 19 Math 141: Lecture 19 Convergence of infinite series Bob Hough November 16, 2016 Bob Hough Math 141: Lecture 19 November 16, 2016 1 / 44 Series of positive terms Recall that, given a sequence {a n } n=1,

More information

Lebesgue-Radon-Nikodym Theorem

Lebesgue-Radon-Nikodym Theorem Lebesgue-Radon-Nikodym Theorem Matt Rosenzweig 1 Lebesgue-Radon-Nikodym Theorem In what follows, (, A) will denote a measurable space. We begin with a review of signed measures. 1.1 Signed Measures Definition

More information

II - REAL ANALYSIS. This property gives us a way to extend the notion of content to finite unions of rectangles: we define

II - REAL ANALYSIS. This property gives us a way to extend the notion of content to finite unions of rectangles: we define 1 Measures 1.1 Jordan content in R N II - REAL ANALYSIS Let I be an interval in R. Then its 1-content is defined as c 1 (I) := b a if I is bounded with endpoints a, b. If I is unbounded, we define c 1

More information

Part V. 17 Introduction: What are measures and why measurable sets. Lebesgue Integration Theory

Part V. 17 Introduction: What are measures and why measurable sets. Lebesgue Integration Theory Part V 7 Introduction: What are measures and why measurable sets Lebesgue Integration Theory Definition 7. (Preliminary). A measure on a set is a function :2 [ ] such that. () = 2. If { } = is a finite

More information

Summer Jump-Start Program for Analysis, 2012 Song-Ying Li. 1 Lecture 7: Equicontinuity and Series of functions

Summer Jump-Start Program for Analysis, 2012 Song-Ying Li. 1 Lecture 7: Equicontinuity and Series of functions Summer Jump-Start Program for Analysis, 0 Song-Ying Li Lecture 7: Equicontinuity and Series of functions. Equicontinuity Definition. Let (X, d) be a metric space, K X and K is a compact subset of X. C(K)

More information

A List of Problems in Real Analysis

A List of Problems in Real Analysis A List of Problems in Real Analysis W.Yessen & T.Ma December 3, 218 This document was first created by Will Yessen, who was a graduate student at UCI. Timmy Ma, who was also a graduate student at UCI,

More information

18.175: Lecture 3 Integration

18.175: Lecture 3 Integration 18.175: Lecture 3 Scott Sheffield MIT Outline Outline Recall definitions Probability space is triple (Ω, F, P) where Ω is sample space, F is set of events (the σ-algebra) and P : F [0, 1] is the probability

More information

REAL ANALYSIS LECTURE NOTES: 1.4 OUTER MEASURE

REAL ANALYSIS LECTURE NOTES: 1.4 OUTER MEASURE REAL ANALYSIS LECTURE NOTES: 1.4 OUTER MEASURE CHRISTOPHER HEIL 1.4.1 Introduction We will expand on Section 1.4 of Folland s text, which covers abstract outer measures also called exterior measures).

More information

4 Countability axioms

4 Countability axioms 4 COUNTABILITY AXIOMS 4 Countability axioms Definition 4.1. Let X be a topological space X is said to be first countable if for any x X, there is a countable basis for the neighborhoods of x. X is said

More information

MATHS 730 FC Lecture Notes March 5, Introduction

MATHS 730 FC Lecture Notes March 5, Introduction 1 INTRODUCTION MATHS 730 FC Lecture Notes March 5, 2014 1 Introduction Definition. If A, B are sets and there exists a bijection A B, they have the same cardinality, which we write as A, #A. If there exists

More information

MATH115. Sequences and Infinite Series. Paolo Lorenzo Bautista. June 29, De La Salle University. PLBautista (DLSU) MATH115 June 29, / 16

MATH115. Sequences and Infinite Series. Paolo Lorenzo Bautista. June 29, De La Salle University. PLBautista (DLSU) MATH115 June 29, / 16 MATH115 Sequences and Infinite Series Paolo Lorenzo Bautista De La Salle University June 29, 2014 PLBautista (DLSU) MATH115 June 29, 2014 1 / 16 Definition A sequence function is a function whose domain

More information

COMPLEX ANALYSIS TOPIC XVI: SEQUENCES. 1. Topology of C

COMPLEX ANALYSIS TOPIC XVI: SEQUENCES. 1. Topology of C COMPLEX ANALYSIS TOPIC XVI: SEQUENCES PAUL L. BAILEY Abstract. We outline the development of sequences in C, starting with open and closed sets, and ending with the statement of the Bolzano-Weierstrauss

More information

2.2 Some Consequences of the Completeness Axiom

2.2 Some Consequences of the Completeness Axiom 60 CHAPTER 2. IMPORTANT PROPERTIES OF R 2.2 Some Consequences of the Completeness Axiom In this section, we use the fact that R is complete to establish some important results. First, we will prove that

More information

Lecture 1: Overview of percolation and foundational results from probability theory 30th July, 2nd August and 6th August 2007

Lecture 1: Overview of percolation and foundational results from probability theory 30th July, 2nd August and 6th August 2007 CSL866: Percolation and Random Graphs IIT Delhi Arzad Kherani Scribe: Amitabha Bagchi Lecture 1: Overview of percolation and foundational results from probability theory 30th July, 2nd August and 6th August

More information

MAT 570 REAL ANALYSIS LECTURE NOTES. Contents. 1. Sets Functions Countability Axiom of choice Equivalence relations 9

MAT 570 REAL ANALYSIS LECTURE NOTES. Contents. 1. Sets Functions Countability Axiom of choice Equivalence relations 9 MAT 570 REAL ANALYSIS LECTURE NOTES PROFESSOR: JOHN QUIGG SEMESTER: FALL 204 Contents. Sets 2 2. Functions 5 3. Countability 7 4. Axiom of choice 8 5. Equivalence relations 9 6. Real numbers 9 7. Extended

More information

MATH 426, TOPOLOGY. p 1.

MATH 426, TOPOLOGY. p 1. MATH 426, TOPOLOGY THE p-norms In this document we assume an extended real line, where is an element greater than all real numbers; the interval notation [1, ] will be used to mean [1, ) { }. 1. THE p

More information