Extension of measure
|
|
- Theresa Parrish
- 8 years ago
- Views:
Transcription
1 1 Extension of measure Sayan Mukherjee Dynkin s π λ theorem We will soon need to define probability measures on infinite and possible uncountable sets, like the power set of the naturals. This is hard. It is easier to define the measure on a much smaller collection of the power set and state consistency conditions that allow us to extend the measure to the the power set. This is what Dynkin s π λ theorem was designed to do. Definition π-system) Given a set Ω a π system is a collection of subsets P that are closed under finite intersections. 1) P is non-empty; 2) A B P whenever A, B P. Definition λ-system) Given a set Ω a λ system is a collection of subsets L that contains Ω and is closed under complementation and disjoint countable unions. 1) Ω L ; 2) A L A c L ; 3) A n L, n 1 with A i A j = i j n=1 A n L. Definition σ-algebra) Let F be a collection of subsets of Ω. F is called a field algebra) if Ω F and F is closed under complementation and countable unions, 1) Ω F; 2) A F A c F; 3) A 1,..., A n F n j=1 A j F; 4) A 1,..., A n,... F j=1 A j F. A σ-algebra is a π system. A σ-algebra is a λ system but a λ system need not be a σ algebra, a λ system is a weaker system. The difference is between unions and disjoint unions. Example Ω = {a, b, c, d} and L = {Ω,, {a, b}, {b, c}, {c, d}, {a, d}, {b, d}, {a, c}} L is closed under disjoint unions but not unions. However: Lemma A class that is both a π system and a λ system is a σ-algebra. Proof. We need to show that the class is closed under countable union. Consider A 1, A 2,..., A n L note that these A i are not disjoint. We disjointify them, B 1 = A 1 and B n = A n \ n 1 A n 1 i = A n Ac i. B n L by π system and complement property of λ system. A i = B i but the {B i } are disjoint so A i = B i L. We will start constructing probability measures on infinite sets and we will need to push the idea of extension of measure. In this the following theorem is central. Theorem Dynkin) 1) If P is a π system and L is a λ system, then P L implies σp) L. b) If P is a π system σp) = L P). Proof of a). Let L 0 be the λ system generated by P, the intersection of all λ systems containing P. This is a λ system and contains P and is contained in every λ system that contains P. So P L 0 L. If L 0 is also a π system, it is a λ system and so is a σ algebra. By minimality of σp) it follows σp) L 0 so P σp) L 0 L. So we need to show that L 0 is a π system. Set A σp) and define G A = {B σp) : A B L P)}.
2 2 1) We show if A L P) then G A is a λ system. i) Ω G A since A Ω = A L P). ii) Suppose B G A. B c A = A\A B) but B G A means A B L P) and since A L P) it holds A \ A B) = B c A L P) by complementarity. Since B c A L P) it holds that B c G A. iii) {B j } are mutually disjoint and B j G A then 2) We show if A P then L P) G A. A B j ) = A B j ) L P). j=1 Since A P L P) from 1) G A is a λ system, j=1 For B P we have A B P since A P and P is a π system. So if B P then A B P L P) implies B G A so P G A and since G A is a λ system L P) G A. 3) We rephrase 2) to state if A P and B L P) then A B L P) if we can extend A to L P) we are done). 4) We show that if A L P) then L P) G A. If B P and A L P) then A B L P) then from 3) it holds A B L P). So when A L P) it holds P G A so from 1) G A is a π system and L P) G A. 5) If A L P) then for any B L P), B G A then A B L P). The following lemma is a result of Dynkin s theorem and highlights uniqueness. Lemma If P i and P 2 are two probability measures on σp) where P is a π system. If P 1 and P 2 agree on P, then they agree on σp). Proof is a λ system and P L so σp) L. L = {A P : P 1 A) = P 2 A)}, We will need to define a probability measure, µ, for all sets in a σ algebra. It will be convenient and almost necessary to define a pre)-measure, µ 0, on a smaller collection F 0 that extends to µ on σf 0 ) and µ is unique, µ 1 F ) = µ 2 F ), F F 0 implies µ 1 F ) = µ 2 F ), F σf 0 ). Restatement of Dynkin s theorem in the above context is that the smallest σ algebra generated from a π system is the λ system generated from that π system. Theorem Let F 0 be a π system then λf 0 ) = σf 0 ). We now define the term extension and develop this idea of extension of measure. The main working example we will use in the development is. Ω = R, S = {a, b] : a b }, Pa, b]) = F b) F a). Remember, that the Borel sets BR) = σs). Goal: we have a probability measure on S we want to extend it to BR). Extension: If G 1 and G 2 are collections of subsets of Ω with G 1 G 2 and P 1 : G 1 [0, 1], P 2 : G 2 [0, 1]. P 2 is an extension of P 1 if P 2 G1 = P 1, P 2 A 1 ) = P 1 A i ) A i G 1.
3 3 Few terms: A measure is P additive if for disjoint sets A 1,..., A n G n A i n n A i G P A i ) = A measure is σ additive if for disjoint sets A 1,..., A n,... G n PA i ). A i G P A i ) = PA i ). Definition Semialgebra) A class S of subsets of Ω is a semialgebra if 1), Ω S 2) S is a π system 3) If A S there exists disjoint sets C 1,..., C n with each C i S such that A c = n C i. Example of Semialgebra: Intervals Ω = R, S = {a, b] : a b }. If I 1, I 2 S then I 1 I 2 S and I c is a union of disjoint intervals. The idea/plan: 1. Start with a semialgebra S 2. Show a unique extension to an algebra AS) generated by S 3. Show a unique extension to σas)) = σs). If S is a semialgebra of Ω then AS) = { S i : I is finite, {S i } is disjoint, S i S}, i I a system of finite collections of mutually disjoint sets. An example of extension of measure for a countable set. Ω = {ω 1,..., ω n,...} i p i p i 0, i = 1 p 1 = 1. F = PΩ) the power set of Ω. We define for all A F the following extension of measure PA) = p i. ω i A For this to be a proper measure the follow need to hold they do) 1. PA) 0 for all A F 2. PΩ) = p i = 1 3. If {A j } with j 1 are disjoint then P Aj ) = j=1 p i = p i = PA j ). w i ja j j ω i A j j Is the power set PΩ) countable? What was the semialgebra above? What would we do if Ω = R? As we consider the following theorems and proofs or proof sketches a good example to keep in mind is the case where Ω = R and S == {a, b] : a < b }.
4 4 Theorem First extension) S is a semialgebra of Ω and P : S [0, 1] is σ-additive on S and PΩ) = 1. There is a unique extension P of P to AS) defined by P S i ) = i PS i ), P Ω) = 1 and P is σ additive on AS). Proof Page 46, Probability Path by Resnick) We need to show that P S i ) = i PS i ), uniquely defines P. Suppose that A AS) has representations A = i I S i = j J S j, we need to verify Since S i A the following hold i I PS i ) = j J PS j). i I PS i ) = i I PS i A) = i I PS i j J = i I P j J S i S j), S j) and S i = j J S i S j S and P is additive on S so and by the same argument PS i ) = PS i S j) = i I i I j J i I PS j) = PS i S j) = j J i I j J i I We now check that P is σ additive on AS). Suppose for i 1, PS i S j), j J PS i S j). j J A i = j J i S ij AS), S ij S, and {A i, i 1} are mutually disjoint and A = A i AS). Since A AS), A can be represented as A = S k, k K S k S, k K, where K is a finite index set. So by the definition of P P A) = k K PS k ). We can write S k = S k A = S k A i = S k S ij. j J i
5 5 Since S k S ij S and j J i S k S ij = S k S and P is σ additive on S PS k ) = PS k S ij ) = PS k S ij ). k K k K j J i k K j J i Note that Sk S ij = A S ij = S ij S, k K and by additivity of P on S we show σ additivity PS k S ij ) = j J i k K = PS ij ) j J i P S ij ) = P A i ). j J i The last step is to check the extension is unique. Assume P 1 and P 2 aretwo additive extensions, then for any A = i I S i AS) we have P 1A) = i I PS i ) = P 2A). We now show how to extend from an algebra to a sigma algebra. Theorem Second extension, Caratheodory) A probability measure P defined on an algebra A has a unique extension to a probability measure P on σa). Proof sketch Page 4, Probability Theory by Varadhan) There are 5 steps to the proof. 1. Define the extension: {A j } are the set of all countable collections of A that are disjoint and we define the outer measure P as P A) = inf A ja j j PA j ). P will be the extension. 2. Show P satisfies properties of a measure i) Countable subattitivity P j A j ) j P A j ). ii) For all A A, P A) = PA). This is done in two steps by showing 1) P A) P A) and 2) P A) PA). 3. Checking measurability of the extension are all of the extra sets in going from the algebra to the σ algebra adding correctly). A set E is measurable if P A) P A E) + P A E c ), for all A A. The class M is the collection of measurable sets. We need to show that M is a σ algebra and P is σ additive on M.
6 6 4. Show A M. This implies that σa) M and P extends from A to σa). 5. Uniqueness. Let P 1 and P 2 be σ additive measures on A then show if for all A A P 1 A) = P 2 A), then P 1 B) = P 2 B), B σa). We now look at two examples. The first is the extension of the Lebesgue measure to Borel sets. The second example illustrates sets that are not measurable. Lebesgue measure: Ω = [0, 1], F = B0, 1]), S = {a, b] : 0 a b 1}. S is a semialgebra and we define the following measure on it λ : S a, b], λ ) = 0, λa, b]) = b a. This measure λ extends to the Borel set B0, 1]). To show that λ extends to B0, 1]) we need to show that it is σ additive on B0, 1]). We first show finite additivity. Set a, b] = K a i, b i ], where a = a 1, b K = b, b i = a i+1. We can see K λa i, b i ] = K b i a i ) = b 1 a 1 + b 2 a 2 + = b K a 1 = b a. We now note a few asymptotic properties of intervals a, b) = [a, b] = {a} = n=1 n=1 n=1 a, b 1 ], a < b n a 1n ], b, a < b a 1n, a ], a < b. The Borel σ algebra contains all singletons a as well as the forms a, b), [a, b], [a, b), a, b]. We now have to address σ additivity. First note that the interval [0, 1] is compact, this means that there exists a finite ε cover of the interval for any ε > 0. Let A n be a sequence of sets from S such that A n. We can define a set B n S such that its closure [B n ] A n and λa n ) λb n ) ε 2 n. Remember A n = and since the sets [B n ] are closed there exists a finite n 0 ε) such that n0 [B n] =. Remember that A n0 A n0 1 A 1 so ) n 0 n0 ) λa n0 ) = λ A n0 \ B k + λ B k n 0 ) = λ A n0 \ B k λ n 0 n0 ) A k \ B k n 0 λa k \ B k ) ε 2 k ε.
7 7 The reason this shows σ additivity is that if for all A n S, A n implies λa n ) 0 then σ additivity holds on σs). Not a measurable set: Let Ω = N be the natural numbers and E be the even numbers and E c the odd numbers and set F = {2 2k + 1,..., 2 2k+1 } = {2,. 5,..., 8, 17,..., 32, 65,..., 128,...}. k=0 1. For n = 2 2k the ratio P n F ) = #[F {1,..., n}]/n = n 1)/3n 1/3 and for n = 2 2k+1, P n F )2n 1)/3n 2/3 so P n cannot converge as n. 2. The even and odd portions of F and F c, A F E and B F c E c, have relative frequencies that do not converge. 3. C = A B does have a relative frequency that converges: P 2n C) = 1/2 + 1/2n so lim n P n = 1/2. 4. Both E and C have well defined asymptotic frequences but A = E C does not and the collection of sets M for which P n converges is not an algebra.
How To Find Out How To Calculate A Premeasure On A Set Of Two-Dimensional Algebra
54 CHAPTER 5 Product Measures Given two measure spaces, we may construct a natural measure on their Cartesian product; the prototype is the construction of Lebesgue measure on R 2 as the product of Lebesgue
More informationBasic Measure Theory. 1.1 Classes of Sets
1 Basic Measure Theory In this chapter, we introduce the classes of sets that allow for a systematic treatment of events and random observations in the framework of probability theory. Furthermore, we
More information1. Prove that the empty set is a subset of every set.
1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: b89902089@ntu.edu.tw Proof: For any element x of the empty set, x is also an element of every set since
More informationDISINTEGRATION OF MEASURES
DISINTEGRTION OF MESURES BEN HES Definition 1. Let (, M, λ), (, N, µ) be sigma-finite measure spaces and let T : be a measurable map. (T, µ)-disintegration is a collection {λ y } y of measures on M such
More informationSOLUTIONS TO ASSIGNMENT 1 MATH 576
SOLUTIONS TO ASSIGNMENT 1 MATH 576 SOLUTIONS BY OLIVIER MARTIN 13 #5. Let T be the topology generated by A on X. We want to show T = J B J where B is the set of all topologies J on X with A J. This amounts
More informationLecture Notes on Measure Theory and Functional Analysis
Lecture Notes on Measure Theory and Functional Analysis P. Cannarsa & T. D Aprile Dipartimento di Matematica Università di Roma Tor Vergata cannarsa@mat.uniroma2.it daprile@mat.uniroma2.it aa 2006/07 Contents
More informationBasic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011
Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011 A. Miller 1. Introduction. The definitions of metric space and topological space were developed in the early 1900 s, largely
More informationElements of probability theory
2 Elements of probability theory Probability theory provides mathematical models for random phenomena, that is, phenomena which under repeated observations yield di erent outcomes that cannot be predicted
More informationProbability: Theory and Examples. Rick Durrett. Edition 4.1, April 21, 2013
i Probability: Theory and Examples Rick Durrett Edition 4.1, April 21, 213 Typos corrected, three new sections in Chapter 8. Copyright 213, All rights reserved. 4th edition published by Cambridge University
More informationBasic Probability Concepts
page 1 Chapter 1 Basic Probability Concepts 1.1 Sample and Event Spaces 1.1.1 Sample Space A probabilistic (or statistical) experiment has the following characteristics: (a) the set of all possible outcomes
More informationMathematics for Econometrics, Fourth Edition
Mathematics for Econometrics, Fourth Edition Phoebus J. Dhrymes 1 July 2012 1 c Phoebus J. Dhrymes, 2012. Preliminary material; not to be cited or disseminated without the author s permission. 2 Contents
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES Contents 1. Random variables and measurable functions 2. Cumulative distribution functions 3. Discrete
More informationFUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES
FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES CHRISTOPHER HEIL 1. Cosets and the Quotient Space Any vector space is an abelian group under the operation of vector addition. So, if you are have studied
More informationLecture Note 1 Set and Probability Theory. MIT 14.30 Spring 2006 Herman Bennett
Lecture Note 1 Set and Probability Theory MIT 14.30 Spring 2006 Herman Bennett 1 Set Theory 1.1 Definitions and Theorems 1. Experiment: any action or process whose outcome is subject to uncertainty. 2.
More informationAn example of a computable
An example of a computable absolutely normal number Verónica Becher Santiago Figueira Abstract The first example of an absolutely normal number was given by Sierpinski in 96, twenty years before the concept
More informationLebesgue Measure on R n
8 CHAPTER 2 Lebesgue Measure on R n Our goal is to construct a notion of the volume, or Lebesgue measure, of rather general subsets of R n that reduces to the usual volume of elementary geometrical sets
More informationMath/Stats 425 Introduction to Probability. 1. Uncertainty and the axioms of probability
Math/Stats 425 Introduction to Probability 1. Uncertainty and the axioms of probability Processes in the real world are random if outcomes cannot be predicted with certainty. Example: coin tossing, stock
More informationMEASURE AND INTEGRATION. Dietmar A. Salamon ETH Zürich
MEASURE AND INTEGRATION Dietmar A. Salamon ETH Zürich 12 May 2016 ii Preface This book is based on notes for the lecture course Measure and Integration held at ETH Zürich in the spring semester 2014. Prerequisites
More informationMA651 Topology. Lecture 6. Separation Axioms.
MA651 Topology. Lecture 6. Separation Axioms. This text is based on the following books: Fundamental concepts of topology by Peter O Neil Elements of Mathematics: General Topology by Nicolas Bourbaki Counterexamples
More informationINDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS
INDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS STEVEN P. LALLEY AND ANDREW NOBEL Abstract. It is shown that there are no consistent decision rules for the hypothesis testing problem
More informationUniversity of Miskolc
University of Miskolc The Faculty of Mechanical Engineering and Information Science The role of the maximum operator in the theory of measurability and some applications PhD Thesis by Nutefe Kwami Agbeko
More information1 if 1 x 0 1 if 0 x 1
Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or
More informationE3: PROBABILITY AND STATISTICS lecture notes
E3: PROBABILITY AND STATISTICS lecture notes 2 Contents 1 PROBABILITY THEORY 7 1.1 Experiments and random events............................ 7 1.2 Certain event. Impossible event............................
More informationMathematical Methods of Engineering Analysis
Mathematical Methods of Engineering Analysis Erhan Çinlar Robert J. Vanderbei February 2, 2000 Contents Sets and Functions 1 1 Sets................................... 1 Subsets.............................
More informationCHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e.
CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e. This chapter contains the beginnings of the most important, and probably the most subtle, notion in mathematical analysis, i.e.,
More informationPoint Set Topology. A. Topological Spaces and Continuous Maps
Point Set Topology A. Topological Spaces and Continuous Maps Definition 1.1 A topology on a set X is a collection T of subsets of X satisfying the following axioms: T 1.,X T. T2. {O α α I} T = α IO α T.
More informationand s n (x) f(x) for all x and s.t. s n is measurable if f is. REAL ANALYSIS Measures. A (positive) measure on a measurable space
RAL ANALYSIS A survey of MA 641-643, UAB 1999-2000 M. Griesemer Throughout these notes m denotes Lebesgue measure. 1. Abstract Integration σ-algebras. A σ-algebra in X is a non-empty collection of subsets
More informationCHAPTER 1 BASIC TOPOLOGY
CHAPTER 1 BASIC TOPOLOGY Topology, sometimes referred to as the mathematics of continuity, or rubber sheet geometry, or the theory of abstract topological spaces, is all of these, but, above all, it is
More informationGENERIC COMPUTABILITY, TURING DEGREES, AND ASYMPTOTIC DENSITY
GENERIC COMPUTABILITY, TURING DEGREES, AND ASYMPTOTIC DENSITY CARL G. JOCKUSCH, JR. AND PAUL E. SCHUPP Abstract. Generic decidability has been extensively studied in group theory, and we now study it in
More informationProbability Theory. Florian Herzog. A random variable is neither random nor variable. Gian-Carlo Rota, M.I.T..
Probability Theory A random variable is neither random nor variable. Gian-Carlo Rota, M.I.T.. Florian Herzog 2013 Probability space Probability space A probability space W is a unique triple W = {Ω, F,
More informationIAM 530 ELEMENTS OF PROBABILITY AND STATISTICS INTRODUCTION
IAM 530 ELEMENTS OF PROBABILITY AND STATISTICS INTRODUCTION 1 WHAT IS STATISTICS? Statistics is a science of collecting data, organizing and describing it and drawing conclusions from it. That is, statistics
More informationINTRODUCTORY SET THEORY
M.Sc. program in mathematics INTRODUCTORY SET THEORY Katalin Károlyi Department of Applied Analysis, Eötvös Loránd University H-1088 Budapest, Múzeum krt. 6-8. CONTENTS 1. SETS Set, equal sets, subset,
More informationSOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties
SOLUTIONS TO EXERCISES FOR MATHEMATICS 205A Part 3 Fall 2008 III. Spaces with special properties III.1 : Compact spaces I Problems from Munkres, 26, pp. 170 172 3. Show that a finite union of compact subspaces
More informationSample Induction Proofs
Math 3 Worksheet: Induction Proofs III, Sample Proofs A.J. Hildebrand Sample Induction Proofs Below are model solutions to some of the practice problems on the induction worksheets. The solutions given
More informationIntroduction to Topology
Introduction to Topology Tomoo Matsumura November 30, 2010 Contents 1 Topological spaces 3 1.1 Basis of a Topology......................................... 3 1.2 Comparing Topologies.......................................
More informationMetric Spaces. Chapter 1
Chapter 1 Metric Spaces Many of the arguments you have seen in several variable calculus are almost identical to the corresponding arguments in one variable calculus, especially arguments concerning convergence
More informationMarkov random fields and Gibbs measures
Chapter Markov random fields and Gibbs measures 1. Conditional independence Suppose X i is a random element of (X i, B i ), for i = 1, 2, 3, with all X i defined on the same probability space (.F, P).
More informationCardinality. The set of all finite strings over the alphabet of lowercase letters is countable. The set of real numbers R is an uncountable set.
Section 2.5 Cardinality (another) Definition: The cardinality of a set A is equal to the cardinality of a set B, denoted A = B, if and only if there is a bijection from A to B. If there is an injection
More informationStatistics in Geophysics: Introduction and Probability Theory
Statistics in Geophysics: Introduction and Steffen Unkel Department of Statistics Ludwig-Maximilians-University Munich, Germany Winter Term 2013/14 1/32 What is Statistics? Introduction Statistics is the
More informationAutomata and Computability. Solutions to Exercises
Automata and Computability Solutions to Exercises Fall 25 Alexis Maciel Department of Computer Science Clarkson University Copyright c 25 Alexis Maciel ii Contents Preface vii Introduction 2 Finite Automata
More information9 More on differentiation
Tel Aviv University, 2013 Measure and category 75 9 More on differentiation 9a Finite Taylor expansion............... 75 9b Continuous and nowhere differentiable..... 78 9c Differentiable and nowhere monotone......
More informationFollow links for Class Use and other Permissions. For more information send email to: permissions@pupress.princeton.edu
COPYRIGHT NOTICE: Ariel Rubinstein: Lecture Notes in Microeconomic Theory is published by Princeton University Press and copyrighted, c 2006, by Princeton University Press. All rights reserved. No part
More informationThis chapter is all about cardinality of sets. At first this looks like a
CHAPTER Cardinality of Sets This chapter is all about cardinality of sets At first this looks like a very simple concept To find the cardinality of a set, just count its elements If A = { a, b, c, d },
More informationI. GROUPS: BASIC DEFINITIONS AND EXAMPLES
I GROUPS: BASIC DEFINITIONS AND EXAMPLES Definition 1: An operation on a set G is a function : G G G Definition 2: A group is a set G which is equipped with an operation and a special element e G, called
More informationThe sample space for a pair of die rolls is the set. The sample space for a random number between 0 and 1 is the interval [0, 1].
Probability Theory Probability Spaces and Events Consider a random experiment with several possible outcomes. For example, we might roll a pair of dice, flip a coin three times, or choose a random real
More informationFIXED POINT SETS OF FIBER-PRESERVING MAPS
FIXED POINT SETS OF FIBER-PRESERVING MAPS Robert F. Brown Department of Mathematics University of California Los Angeles, CA 90095 e-mail: rfb@math.ucla.edu Christina L. Soderlund Department of Mathematics
More informationAutomata on Infinite Words and Trees
Automata on Infinite Words and Trees Course notes for the course Automata on Infinite Words and Trees given by Dr. Meghyn Bienvenu at Universität Bremen in the 2009-2010 winter semester Last modified:
More informationHow To Prove The Dirichlet Unit Theorem
Chapter 6 The Dirichlet Unit Theorem As usual, we will be working in the ring B of algebraic integers of a number field L. Two factorizations of an element of B are regarded as essentially the same if
More informationOmega Automata: Minimization and Learning 1
Omega Automata: Minimization and Learning 1 Oded Maler CNRS - VERIMAG Grenoble, France 2007 1 Joint work with A. Pnueli, late 80s Summary Machine learning in general and of formal languages in particular
More informationNotes on metric spaces
Notes on metric spaces 1 Introduction The purpose of these notes is to quickly review some of the basic concepts from Real Analysis, Metric Spaces and some related results that will be used in this course.
More informationDedekind s forgotten axiom and why we should teach it (and why we shouldn t teach mathematical induction in our calculus classes)
Dedekind s forgotten axiom and why we should teach it (and why we shouldn t teach mathematical induction in our calculus classes) by Jim Propp (UMass Lowell) March 14, 2010 1 / 29 Completeness Three common
More informationPeople have thought about, and defined, probability in different ways. important to note the consequences of the definition:
PROBABILITY AND LIKELIHOOD, A BRIEF INTRODUCTION IN SUPPORT OF A COURSE ON MOLECULAR EVOLUTION (BIOL 3046) Probability The subject of PROBABILITY is a branch of mathematics dedicated to building models
More informationGod created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886)
Chapter 2 Numbers God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886) God created the integers and the rest is the work
More informationThe Banach-Tarski Paradox
University of Oslo MAT2 Project The Banach-Tarski Paradox Author: Fredrik Meyer Supervisor: Nadia S. Larsen Abstract In its weak form, the Banach-Tarski paradox states that for any ball in R, it is possible
More informationNotes V General Equilibrium: Positive Theory. 1 Walrasian Equilibrium and Excess Demand
Notes V General Equilibrium: Positive Theory In this lecture we go on considering a general equilibrium model of a private ownership economy. In contrast to the Notes IV, we focus on positive issues such
More informationMinimal R 1, minimal regular and minimal presober topologies
Revista Notas de Matemática Vol.5(1), No. 275, 2009, pp.73-84 http://www.saber.ula.ve/notasdematematica/ Comisión de Publicaciones Departamento de Matemáticas Facultad de Ciencias Universidad de Los Andes
More informationGENERATING SETS KEITH CONRAD
GENERATING SETS KEITH CONRAD 1 Introduction In R n, every vector can be written as a unique linear combination of the standard basis e 1,, e n A notion weaker than a basis is a spanning set: a set of vectors
More information1 Norms and Vector Spaces
008.10.07.01 1 Norms and Vector Spaces Suppose we have a complex vector space V. A norm is a function f : V R which satisfies (i) f(x) 0 for all x V (ii) f(x + y) f(x) + f(y) for all x,y V (iii) f(λx)
More informationThe Henstock-Kurzweil-Stieltjes type integral for real functions on a fractal subset of the real line
The Henstock-Kurzweil-Stieltjes type integral for real functions on a fractal subset of the real line D. Bongiorno, G. Corrao Dipartimento di Ingegneria lettrica, lettronica e delle Telecomunicazioni,
More informationMATHEMATICAL METHODS OF STATISTICS
MATHEMATICAL METHODS OF STATISTICS By HARALD CRAMER TROFESSOK IN THE UNIVERSITY OF STOCKHOLM Princeton PRINCETON UNIVERSITY PRESS 1946 TABLE OF CONTENTS. First Part. MATHEMATICAL INTRODUCTION. CHAPTERS
More informationMetric Spaces Joseph Muscat 2003 (Last revised May 2009)
1 Distance J Muscat 1 Metric Spaces Joseph Muscat 2003 (Last revised May 2009) (A revised and expanded version of these notes are now published by Springer.) 1 Distance A metric space can be thought of
More informationLECTURE NOTES IN MEASURE THEORY. Christer Borell Matematik Chalmers och Göteborgs universitet 412 96 Göteborg (Version: January 12)
1 LECTURE NOTES IN MEASURE THEORY Christer Borell Matematik Chalmers och Göteborgs universitet 412 96 Göteborg (Version: January 12) 2 PREFACE These are lecture notes on integration theory for a eight-week
More informationToday s Topics. Primes & Greatest Common Divisors
Today s Topics Primes & Greatest Common Divisors Prime representations Important theorems about primality Greatest Common Divisors Least Common Multiples Euclid s algorithm Once and for all, what are prime
More informationPoisson process. Etienne Pardoux. Aix Marseille Université. Etienne Pardoux (AMU) CIMPA, Ziguinchor 1 / 8
Poisson process Etienne Pardoux Aix Marseille Université Etienne Pardoux (AMU) CIMPA, Ziguinchor 1 / 8 The standard Poisson process Let λ > be given. A rate λ Poisson (counting) process is defined as P
More informationINTRODUCTION TO TOPOLOGY
INTRODUCTION TO TOPOLOGY ALEX KÜRONYA In preparation January 24, 2010 Contents 1. Basic concepts 1 2. Constructing topologies 13 2.1. Subspace topology 13 2.2. Local properties 18 2.3. Product topology
More informationLemma 5.2. Let S be a set. (1) Let f and g be two permutations of S. Then the composition of f and g is a permutation of S.
Definition 51 Let S be a set bijection f : S S 5 Permutation groups A permutation of S is simply a Lemma 52 Let S be a set (1) Let f and g be two permutations of S Then the composition of f and g is a
More informationExample 4.1 (nonlinear pendulum dynamics with friction) Figure 4.1: Pendulum. asin. k, a, and b. We study stability of the origin x
Lecture 4. LaSalle s Invariance Principle We begin with a motivating eample. Eample 4.1 (nonlinear pendulum dynamics with friction) Figure 4.1: Pendulum Dynamics of a pendulum with friction can be written
More informationSolutions to TOPICS IN ALGEBRA I.N. HERSTEIN. Part II: Group Theory
Solutions to TOPICS IN ALGEBRA I.N. HERSTEIN Part II: Group Theory No rights reserved. Any part of this work can be reproduced or transmitted in any form or by any means. Version: 1.1 Release: Jan 2013
More informationTHE FUNDAMENTAL THEOREM OF ALGEBRA VIA PROPER MAPS
THE FUNDAMENTAL THEOREM OF ALGEBRA VIA PROPER MAPS KEITH CONRAD 1. Introduction The Fundamental Theorem of Algebra says every nonconstant polynomial with complex coefficients can be factored into linear
More informationNo: 10 04. Bilkent University. Monotonic Extension. Farhad Husseinov. Discussion Papers. Department of Economics
No: 10 04 Bilkent University Monotonic Extension Farhad Husseinov Discussion Papers Department of Economics The Discussion Papers of the Department of Economics are intended to make the initial results
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +
More informationn k=1 k=0 1/k! = e. Example 6.4. The series 1/k 2 converges in R. Indeed, if s n = n then k=1 1/k, then s 2n s n = 1 n + 1 +...
6 Series We call a normed space (X, ) a Banach space provided that every Cauchy sequence (x n ) in X converges. For example, R with the norm = is an example of Banach space. Now let (x n ) be a sequence
More informationCODING TRUE ARITHMETIC IN THE MEDVEDEV AND MUCHNIK DEGREES
CODING TRUE ARITHMETIC IN THE MEDVEDEV AND MUCHNIK DEGREES PAUL SHAFER Abstract. We prove that the first-order theory of the Medvedev degrees, the first-order theory of the Muchnik degrees, and the third-order
More informationProbability for Estimation (review)
Probability for Estimation (review) In general, we want to develop an estimator for systems of the form: x = f x, u + η(t); y = h x + ω(t); ggggg y, ffff x We will primarily focus on discrete time linear
More informationTOPOLOGY: THE JOURNEY INTO SEPARATION AXIOMS
TOPOLOGY: THE JOURNEY INTO SEPARATION AXIOMS VIPUL NAIK Abstract. In this journey, we are going to explore the so called separation axioms in greater detail. We shall try to understand how these axioms
More informationHow To Understand The Theory Of Hyperreals
Ultraproducts and Applications I Brent Cody Virginia Commonwealth University September 2, 2013 Outline Background of the Hyperreals Filters and Ultrafilters Construction of the Hyperreals The Transfer
More informationReference: Introduction to Partial Differential Equations by G. Folland, 1995, Chap. 3.
5 Potential Theory Reference: Introduction to Partial Differential Equations by G. Folland, 995, Chap. 3. 5. Problems of Interest. In what follows, we consider Ω an open, bounded subset of R n with C 2
More informationDescriptive Set Theory
Descriptive Set Theory David Marker Fall 2002 Contents I Classical Descriptive Set Theory 2 1 Polish Spaces 2 2 Borel Sets 14 3 Effective Descriptive Set Theory: The Arithmetic Hierarchy 27 4 Analytic
More informationThe Markov-Zariski topology of an infinite group
Mimar Sinan Güzel Sanatlar Üniversitesi Istanbul January 23, 2014 joint work with Daniele Toller and Dmitri Shakhmatov 1. Markov s problem 1 and 2 2. The three topologies on an infinite group 3. Problem
More informationarxiv:math/0510680v3 [math.gn] 31 Oct 2010
arxiv:math/0510680v3 [math.gn] 31 Oct 2010 MENGER S COVERING PROPERTY AND GROUPWISE DENSITY BOAZ TSABAN AND LYUBOMYR ZDOMSKYY Abstract. We establish a surprising connection between Menger s classical covering
More informationMath 104: Introduction to Analysis
Math 104: Introduction to Analysis Evan Chen UC Berkeley Notes for the course MATH 104, instructed by Charles Pugh. 1 1 August 29, 2013 Hard: #22 in Chapter 1. Consider a pile of sand principle. You wish
More informationCommentationes Mathematicae Universitatis Carolinae
Commentationes Mathematicae Universitatis Carolinae Adam Emeryk; Władysław Kulpa The Sorgenfrey line has no connected compactification Commentationes Mathematicae Universitatis Carolinae, Vol. 18 (1977),
More informationOperator-valued version of conditionally free product
STUDIA MATHEMATICA 153 (1) (2002) Operator-valued version of conditionally free product by Wojciech Młotkowski (Wrocław) Abstract. We present an operator-valued version of the conditionally free product
More informationNotes on descriptive set theory and applications to Banach spaces. Th. Schlumprecht
Notes on descriptive set theory and applications to Banach spaces Th. Schlumprecht Contents Chapter 1. Notation 5 Chapter 2. Polish spaces 7 1. Introduction 7 2. Transfer Theorems 9 3. Spaces of compact
More informationProbability and statistics; Rehearsal for pattern recognition
Probability and statistics; Rehearsal for pattern recognition Václav Hlaváč Czech Technical University in Prague Faculty of Electrical Engineering, Department of Cybernetics Center for Machine Perception
More informationStationary random graphs on Z with prescribed iid degrees and finite mean connections
Stationary random graphs on Z with prescribed iid degrees and finite mean connections Maria Deijfen Johan Jonasson February 2006 Abstract Let F be a probability distribution with support on the non-negative
More information6.3 Conditional Probability and Independence
222 CHAPTER 6. PROBABILITY 6.3 Conditional Probability and Independence Conditional Probability Two cubical dice each have a triangle painted on one side, a circle painted on two sides and a square painted
More informationChapter 3. Cartesian Products and Relations. 3.1 Cartesian Products
Chapter 3 Cartesian Products and Relations The material in this chapter is the first real encounter with abstraction. Relations are very general thing they are a special type of subset. After introducing
More informationDEGREES OF ORDERS ON TORSION-FREE ABELIAN GROUPS
DEGREES OF ORDERS ON TORSION-FREE ABELIAN GROUPS ASHER M. KACH, KAREN LANGE, AND REED SOLOMON Abstract. We construct two computable presentations of computable torsion-free abelian groups, one of isomorphism
More informationFACTORING CERTAIN INFINITE ABELIAN GROUPS BY DISTORTED CYCLIC SUBSETS
International Electronic Journal of Algebra Volume 6 (2009) 95-106 FACTORING CERTAIN INFINITE ABELIAN GROUPS BY DISTORTED CYCLIC SUBSETS Sándor Szabó Received: 11 November 2008; Revised: 13 March 2009
More informationPSEUDOARCS, PSEUDOCIRCLES, LAKES OF WADA AND GENERIC MAPS ON S 2
PSEUDOARCS, PSEUDOCIRCLES, LAKES OF WADA AND GENERIC MAPS ON S 2 Abstract. We prove a Bruckner-Garg type theorem for the fiber structure of a generic map from a continuum X into the unit interval I. We
More information4: SINGLE-PERIOD MARKET MODELS
4: SINGLE-PERIOD MARKET MODELS Ben Goldys and Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2015 B. Goldys and M. Rutkowski (USydney) Slides 4: Single-Period Market
More informationA Short Proof that Compact 2-Manifolds Can Be Triangulated
Inventiones math. 5, 160--162 (1968) A Short Proof that Compact 2-Manifolds Can Be Triangulated P. H. DOYLE and D. A. MORAN* (East Lansing, Michigan) The result mentioned in the title of this paper was
More informationUndergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics
Undergraduate Notes in Mathematics Arkansas Tech University Department of Mathematics An Introductory Single Variable Real Analysis: A Learning Approach through Problem Solving Marcel B. Finan c All Rights
More informationDeterministic Finite Automata
1 Deterministic Finite Automata Definition: A deterministic finite automaton (DFA) consists of 1. a finite set of states (often denoted Q) 2. a finite set Σ of symbols (alphabet) 3. a transition function
More informationChapter 4. Probability and Probability Distributions
Chapter 4. robability and robability Distributions Importance of Knowing robability To know whether a sample is not identical to the population from which it was selected, it is necessary to assess the
More informationGambling Systems and Multiplication-Invariant Measures
Gambling Systems and Multiplication-Invariant Measures by Jeffrey S. Rosenthal* and Peter O. Schwartz** (May 28, 997.. Introduction. This short paper describes a surprising connection between two previously
More informationLecture 2: Universality
CS 710: Complexity Theory 1/21/2010 Lecture 2: Universality Instructor: Dieter van Melkebeek Scribe: Tyson Williams In this lecture, we introduce the notion of a universal machine, develop efficient universal
More informationTaylor and Maclaurin Series
Taylor and Maclaurin Series In the preceding section we were able to find power series representations for a certain restricted class of functions. Here we investigate more general problems: Which functions
More informationDoug Ravenel. October 15, 2008
Doug Ravenel University of Rochester October 15, 2008 s about Euclid s Some s about primes that every mathematician should know (Euclid, 300 BC) There are infinitely numbers. is very elementary, and we
More information