Chapter 0. Review of set theory. 0.1 Sets
|
|
- Jean Bradford
- 5 years ago
- Views:
Transcription
1 Chapter 0 Review of set theory Set theory plays a cetral role i the theory of probability. Thus, we will ope this course with a quick review of those otios of set theory which will be used repeatedly. 0.1 Sets Throughout this chapter, let X be a set, which we will call the whole space. The elemets of X will ofte be called poits. If A is a subset of X ad x is a poit i X, the the otatio x A meas that x belogs to A, i.e., it is oe of the poits i A. If x does ot belog to A, we write x A. Wheever the idetity of the whole space X is clear, we will refer to subsets of X simples as sets. If A ad B are sets, the otatios A B or B A mea that A is a subset of B, i.e., every poit i A is also a poit i B. Two sets are called equal if they cotai exactly the same set of poits, i.e., A B ad B A.
2 6 Chapter 0 Whe cosiderig the collectio of all subsets of X, we always iclude the empty set, which is deoted by. By defiitio, For every set A, it always holds that x X, x. A X. We will ofte cosider sets of sets. To avoid cofusio, we will use the term a collectio (42&!) of sets, rather the a set of sets; however remember, a collectio is othig but a set. Thus, is C is a collectio of sets ad A is a set, the A C meas that the set A is oe of the elemets of the collectio of sets C. The otatio A C, o the other had, is meaigless. Example: Let X be a whole space. The collectio of all its subsets is deoted by 2 X ; it is called the power set of X (%8'(% ;7&"8). This otatio is suggestive, as if X has fiite cardiality X, the 2 X = 2 X. Defiitio 0.1 A sequece (A ) of sets is called icreasig (%-&3) if It is called decreasig (;$9&*) if I either case it is called mootoe. A 1 A 2 A 3. A 1 A 2 A 3. To deote sets, we will use curly brackets. For example, if x, y X, the {x, y} deotes the set whose oly elemets are x ad y. If (x k ) k=1 is a fiite sequece of distict poits i X, we will deote the set whose oly elemets are the elemets of that sequece by {x k k = 1,...,}.
3 Review of set theory 7 If (x k ) is a ifiite sequece of distict poits i X, we will deote the set whose oly elemets are the elemets of that sequece by {x k k N}. Suppose that for every x X, (x) is a propositio assumig values either True or False. The, {x X (x)} deotes the set of all poits i X for which the propositio (x) is True. More geerally, if 1 (x), 2 (x),... is a sequece of propositios regardig the poit x, the {x X 1 (x), 2 (x),...} deotes the set of all poits i X for which all the propositios k (x) are True. Example: Let X = N ad for N let True () = False is eve is odd. The, { N ()} deotes the set of eve itegers. I may istaces, we will use the more ituitive otatio { N is eve}. As a tautological, yet useful remark: every set A ca be expressed usig the curly bracket otatio as A = {x X x A}. Fially, for every x X, {x} is a subset of X, i.e., a elemet of 2 X ; such a subset is called a sigleto (0&$*(*). Be careful to distiguish x from {x}.
4 8 Chapter Uios ad itersectios Let C be a collectio of subsets of X. The subset of X icludig all those poits that belog to at least oe set of the collectio C is called the uio ($&(*!) of the collectio, ad it is deoted by C or {A A C}. I the case where the collectio C icludes oly two sets, e.g., C = {A, B}, we simply write C = A B. If C = {A i is a fiite collectio of sets, we write If i = 1,...,} C = A i. i=1 C = {A i i N} is a coutable collectio of sets, we write Fially, if C = A i. i=1 C = {A } is a o-coutable collectio of sets, where C = A. By covetio if C is a empty collectio of sets, the C =. It is easy to see that wheever A B, the A B = B. is a idex set, we write
5 Review of set theory 9 I particular, it always holds that A = A ad A X = X. Moreover, if (A ) is a icreasig sequece of sets, the k=1 A k = A. Let C be a collectio of subsets of X. The subset of X icludig all those poits that belog to all sets of the collectio C is called the itersectio (+&;*() of the collectio, ad it is deoted by C or {A A C}. I the case where the collectio C icludes oly two sets, e.g., C = {A, B}, we simply write C = A B. If C = {A i is a fiite collectio of sets, we write If i = 1,...,} C = A i. i=1 C = {A i i N} is a coutable collectio of sets, we write Fially, if C = A i. i=1 C = {A } is a o-coutable collectio of sets, where C = A. is a idex set, we write
6 10 Chapter 0 By covetio if C is a empty collectio of sets the C = X. It is easy to see that wheever A B, the I particular, it always holds that A B = A. A = ad A X = A. Moreover, if (A ) is a decreasig sequece of sets, the k=1 A k = A. Defiitio 0.2 Two sets A ad B are called disjoit (.*9') if they have o poits i commo, i.e., A B =. Adisjoit collectio of sets is a collectio of sets such that every two distict sets i this collectio are disjoit. Sice uios of disjoit collectios of sets play a importat role i the theory of probability, we will deote the uio of a disjoit collectio by. For example, A B deotes the uio of the disjoit sets A ad B. Uios ad itersectios satisfy the followig importat properties: 1. Both are commutative ad associative. 2. Itersectios are distributive over uios, A (B C) = (A B) (A C). 3. Uios are distributive over itersectios, A (B C) = (A B) (A C).
7 Review of set theory Complemets Let A be a set. It complemet (.*-:/) is the set of poits which do ot belog to A, A c = {x x A}. Complemetatio satisfies the followig properties: 1. For all sets A, A A c = X ad A A c =. 2. For all sets A, (A c ) c = A. 3. c = X ad X c =. 4. If A B the A c B c. 5. For every (possibly o-coutable) collectio C of sets, ad ({A A C}) c = {A c ({A A C}) c = {A c A C}, A C}. Let A ad B be sets. The di erece betwee A ad B is the set of all those poits that are i A but ot i B, amely, A B = {x x A ad x B} = A B c. 0.4 Limits Let (A ) be a sequece of sets. The superior limit (0&*-3 -&"#) of this sequece is defied as the set of poits that belog to ifiitely may of those sets: lim sup A = {x for all k there exists a k such that x A }. If x lim sup A we say that x A ifiitely ofte (%1:1 05&!"). The iferior limit (0&;(; -&"#) of this sequece is defied as the set of poits that belog to all but fiitely may of those sets: lim if A = {x there exists k such that x A for all k}.
8 12 Chapter 0 If x lim sup A we say that x A evetually ($*/; 9"$ -: &5&2"). Note that both superior ad iferior limits always exist. Also, it always holds that lim if A lim sup A. I the evet that the superior limit ad the iferior limit of a sequece of sets coicide, we call this set the limit of the sequece, amely, lim A = lim sup A = lim if A. By defiitio, for k N, =k A = {x there exists a k such that x A }, hece, Likewise, for k N, k=1 =k A = lim sup A. =k A = {x x A for all k}, hece, k=1 =k A = lim if A. Propositio 0.1 If (A ) is a mootoe sequece of sets, the it has a limit. Specifically, if (A ) is icreasig the lim A = A. =1 ad if (A ) is decreasig the lim A = A. =1
9 Review of set theory 13 Proof : Let (A ) be icreasig. The for every k N, Thus, ad =k A = A =1 lim sup ad A = lim if k=1 =1 =k A = A, =1 A = A. k=1 Let (A ) be decreasig. The for every k N, Thus, ad =k A = A =1 lim if lim sup A = ad A = A k, k=1 k=1 =1 =k A = A. =1 A k. A k. Example: Let X be the set of itegers, N, ad let {2, 4, 6,...} is eve A = {1, 3, 5,...} is odd. The, lim sup A = X ad lim if A =, i.e., the sequece (A ) does ot have a limit. Example: Let agai X = N ad let amely, etc. The, A k = k j j = 0, 1, 2,..., A 1 = {1} A 2 = {1, 2, 4,...} A 2 = {1, 3, 9,...}, lim k A k = {1}.
10 14 Chapter Algebras ad -algebras of sets Defiitio 0.3 A collectio C of sets is called a algebra of sets (-: %9"#-! ;&7&"8) if it satifsies the followig properties: 1. If A C the A c C. 2. If A, B C the A B C. 3. X C. Propositio 0.2 Let C be a algebra of sets. The, 1. C. 2. If A 1,...,A C the i=1 A i C. 3. If A 1,...,A C the i=1 A i C. 4. Let A, B C. The their di erece A B is i C. Proof : Sice C is closed uder complemetatio ad X C, = X c C. The secod assertio follows by iductio. The third assertio follows from the secod assertio ad de Morga s Law, i=1 The fourth assertio holds because A i = c A c i C. i=1 A B = A B c. Thus, a algebra of sets is a collectio of sets closed uder fiitely may settheoretic operatios of uio, itersectio ad complemetatio.
11 Review of set theory 15 Example: Let A be a set. The, the collectio of sets C = {, A, A c, X} is a algebra of sets. Example: The collectio of all subsets 2 X is a algebra of sets. Example: Suppose that X is a ifiite set. The collectio of all fiite subsets of X is ot a algebra of sets. Neither is the collectio of all ifiite subsets of X. Defiitio 0.4 A collectio C of sets is called a -algebra of sets if it is a algebra of sets, ad i additio, if (A ) is a sequece of sets i C, the A C. =1 Propositio 0.3 Let C be a algebra of sets. If (A ) is a sequece of sets i C, the =1 A C. Proof : This is a immediate cosequece of the idetity A = c A c. =1 =1 That is, a -algebra of sets is closed with respect to coutably may set-theoretic operatios. Example: The collectio of all subsets 2 X is a -algebra of sets.
12 16 Chapter 0 Propositio 0.4 Let C, be a (possible o-coutable) family of -algebras of sets. The, their itersectio, F = {C } is also a -algebra of sets. Proof : Let (A ) be a sequece of sets i F. By defiitio, A C for every. Sice C is a -algebra, the X C, A c C ad A C. =1 This this holds for every, the X F, A c F ad A F. =1 Defiitio 0.5 Let C be a o-empty collectio of sets. The by C is the itersectio of all those -algebras cotaiig C. -algebra geerated (C) = {A A is a -algebra ad C A}. Sice 2 X is a -algebra cotaiig C, this itersectio is ot empty. By Propositio 0.4, (C) is a -algebra. Example: Let A be a set. The, ({A}) = {, A, A c, X}.. Exercise 0.1 Let A, B be sets. Fid ({A, B}).
13 0.6 Iverse fuctios Review of set theory 17 I calculus, you leared that a fuctio f X Y is ivertible oly if it is both oe-to-oe ad oto; i this case, we ca defie f 1 Y X. A iverse fuctio, however, ca always be defied as a mappig betwee sets. For every fuctio f X Y we ca defie f 1 2 Y 2 X by f 1 (A) = {x X f (x) A}, A Y. A importat property of the iverse fuctio f 1 is that it preserves (commutes with) set-theoretic operatios: Propositio 0.5 Let f X Y. The, 1. For every A Y ( f 1 (A)) c = f 1 (A c ). 2. If A, B Y are disjoit so are f 1 (A), f 1 (B) X. 3. f 1 (Y) = X. 4. If A Y is a sequece of subsets, the f 1 A = f 1 (A ). =1 =1 Proof : Just follow the defiitios. For example, x f 1 (A) i f (x) A, hece x ( f 1 (A)) c i f (x) A i f (x) A c i x f 1 (A c ).
14 18 Chapter 0 The fact that iverse fuctios commute with set-theoretic operatios has the followig implicatio. Let X ad Y be sets ad let f X Y. Let F 2 Y be a -algebra of subsets of Y, the is a -algebra of subsets of X. { f 1 (A) A F }
Here are some examples of algebras: F α = A(G). Also, if A, B A(G) then A, B F α. F α = A(G). In other words, A(G)
MATH 529 Probability Axioms Here we shall use the geeral axioms of a probability measure to derive several importat results ivolvig probabilities of uios ad itersectios. Some more advaced results will
More informationAxioms of Measure Theory
MATH 532 Axioms of Measure Theory Dr. Neal, WKU I. The Space Throughout the course, we shall let X deote a geeric o-empty set. I geeral, we shall ot assume that ay algebraic structure exists o X so that
More informationThe Boolean Ring of Intervals
MATH 532 Lebesgue Measure Dr. Neal, WKU We ow shall apply the results obtaied about outer measure to the legth measure o the real lie. Throughout, our space X will be the set of real umbers R. Whe ecessary,
More informationIt is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function.
MATH 532 Measurable Fuctios Dr. Neal, WKU Throughout, let ( X, F, µ) be a measure space ad let (!, F, P ) deote the special case of a probability space. We shall ow begi to study real-valued fuctios defied
More informationIf a subset E of R contains no open interval, is it of zero measure? For instance, is the set of irrationals in [0, 1] is of measure zero?
2 Lebesgue Measure I Chapter 1 we defied the cocept of a set of measure zero, ad we have observed that every coutable set is of measure zero. Here are some atural questios: If a subset E of R cotais a
More informationSolution. 1 Solutions of Homework 1. Sangchul Lee. October 27, Problem 1.1
Solutio Sagchul Lee October 7, 017 1 Solutios of Homework 1 Problem 1.1 Let Ω,F,P) be a probability space. Show that if {A : N} F such that A := lim A exists, the PA) = lim PA ). Proof. Usig the cotiuity
More informationMeasure and Measurable Functions
3 Measure ad Measurable Fuctios 3.1 Measure o a Arbitrary σ-algebra Recall from Chapter 2 that the set M of all Lebesgue measurable sets has the followig properties: R M, E M implies E c M, E M for N implies
More informationThe Borel hierarchy classifies subsets of the reals by their topological complexity. Another approach is to classify them by size.
Lecture 7: Measure ad Category The Borel hierarchy classifies subsets of the reals by their topological complexity. Aother approach is to classify them by size. Filters ad Ideals The most commo measure
More informationExercises 1 Sets and functions
Exercises 1 Sets ad fuctios HU Wei September 6, 018 1 Basics Set theory ca be made much more rigorous ad built upo a set of Axioms. But we will cover oly some heuristic ideas. For those iterested studets,
More informationProduct measures, Tonelli s and Fubini s theorems For use in MAT3400/4400, autumn 2014 Nadia S. Larsen. Version of 13 October 2014.
Product measures, Toelli s ad Fubii s theorems For use i MAT3400/4400, autum 2014 Nadia S. Larse Versio of 13 October 2014. 1. Costructio of the product measure The purpose of these otes is to preset the
More informationLecture Notes for Analysis Class
Lecture Notes for Aalysis Class Topological Spaces A topology for a set X is a collectio T of subsets of X such that: (a) X ad the empty set are i T (b) Uios of elemets of T are i T (c) Fiite itersectios
More informationIntroduction to Probability. Ariel Yadin. Lecture 2
Itroductio to Probability Ariel Yadi Lecture 2 1. Discrete Probability Spaces Discrete probability spaces are those for which the sample space is coutable. We have already see that i this case we ca take
More informationLecture 10: Mathematical Preliminaries
Lecture : Mathematical Prelimiaries Obective: Reviewig mathematical cocepts ad tools that are frequetly used i the aalysis of algorithms. Lecture # Slide # I this
More information2.4 Sequences, Sequences of Sets
72 CHAPTER 2. IMPORTANT PROPERTIES OF R 2.4 Sequeces, Sequeces of Sets 2.4.1 Sequeces Defiitio 2.4.1 (sequece Let S R. 1. A sequece i S is a fuctio f : K S where K = { N : 0 for some 0 N}. 2. For each
More information1 Introduction. 1.1 Notation and Terminology
1 Itroductio You have already leared some cocepts of calculus such as limit of a sequece, limit, cotiuity, derivative, ad itegral of a fuctio etc. Real Aalysis studies them more rigorously usig a laguage
More informationM17 MAT25-21 HOMEWORK 5 SOLUTIONS
M17 MAT5-1 HOMEWORK 5 SOLUTIONS 1. To Had I Cauchy Codesatio Test. Exercise 1: Applicatio of the Cauchy Codesatio Test Use the Cauchy Codesatio Test to prove that 1 diverges. Solutio 1. Give the series
More informationLecture 3 : Random variables and their distributions
Lecture 3 : Radom variables ad their distributios 3.1 Radom variables Let (Ω, F) ad (S, S) be two measurable spaces. A map X : Ω S is measurable or a radom variable (deoted r.v.) if X 1 (A) {ω : X(ω) A}
More informationChapter 3. Strong convergence. 3.1 Definition of almost sure convergence
Chapter 3 Strog covergece As poited out i the Chapter 2, there are multiple ways to defie the otio of covergece of a sequece of radom variables. That chapter defied covergece i probability, covergece i
More informationIntroduction to Probability. Ariel Yadin. Lecture 7
Itroductio to Probability Ariel Yadi Lecture 7 1. Idepedece Revisited 1.1. Some remiders. Let (Ω, F, P) be a probability space. Give a collectio of subsets K F, recall that the σ-algebra geerated by K,
More informationSolutions to Math 347 Practice Problems for the final
Solutios to Math 347 Practice Problems for the fial 1) True or False: a) There exist itegers x,y such that 50x + 76y = 6. True: the gcd of 50 ad 76 is, ad 6 is a multiple of. b) The ifiimum of a set is
More informationSingular Continuous Measures by Michael Pejic 5/14/10
Sigular Cotiuous Measures by Michael Peic 5/4/0 Prelimiaries Give a set X, a σ-algebra o X is a collectio of subsets of X that cotais X ad ad is closed uder complemetatio ad coutable uios hece, coutable
More informationNotes #3 Sequences Limit Theorems Monotone and Subsequences Bolzano-WeierstraßTheorem Limsup & Liminf of Sequences Cauchy Sequences and Completeness
Notes #3 Sequeces Limit Theorems Mootoe ad Subsequeces Bolzao-WeierstraßTheorem Limsup & Limif of Sequeces Cauchy Sequeces ad Completeess This sectio of otes focuses o some of the basics of sequeces of
More informationFUNDAMENTALS OF REAL ANALYSIS by. V.1. Product measures
FUNDAMENTALS OF REAL ANALSIS by Doğa Çömez V. PRODUCT MEASURE SPACES V.1. Product measures Let (, A, µ) ad (, B, ν) be two measure spaces. I this sectio we will costruct a product measure µ ν o that coicides
More information(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3
MATH 337 Sequeces Dr. Neal, WKU Let X be a metric space with distace fuctio d. We shall defie the geeral cocept of sequece ad limit i a metric space, the apply the results i particular to some special
More informationLecture 2 Measures. Measure spaces. µ(a n ), for n N, and pairwise disjoint A 1,..., A n, we say that the. (S, S) is called
Lecture 2: Measures 1 of 17 Course: Theory of Probability I Term: Fall 2013 Istructor: Gorda Zitkovic Lecture 2 Measures Measure spaces Defiitio 2.1 (Measure). Let (S, S) be a measurable space. A mappig
More informationHOMEWORK #4 - MA 504
HOMEWORK #4 - MA 504 PAULINHO TCHATCHATCHA Chapter 2, problem 19. (a) If A ad B are disjoit closed sets i some metric space X, prove that they are separated. (b) Prove the same for disjoit ope set. (c)
More informationSequences and Series of Functions
Chapter 6 Sequeces ad Series of Fuctios 6.1. Covergece of a Sequece of Fuctios Poitwise Covergece. Defiitio 6.1. Let, for each N, fuctio f : A R be defied. If, for each x A, the sequece (f (x)) coverges
More informationConvergence of random variables. (telegram style notes) P.J.C. Spreij
Covergece of radom variables (telegram style otes).j.c. Spreij this versio: September 6, 2005 Itroductio As we kow, radom variables are by defiitio measurable fuctios o some uderlyig measurable space
More informationWhat is Probability?
Quatificatio of ucertaity. What is Probability? Mathematical model for thigs that occur radomly. Radom ot haphazard, do t kow what will happe o ay oe experimet, but has a log ru order. The cocept of probability
More informationSets and Probabilistic Models
ets ad Probabilistic Models Berli Che Departmet of Computer ciece & Iformatio Egieerig Natioal Taiwa Normal iversity Referece: - D. P. Bertsekas, J. N. Tsitsiklis, Itroductio to Probability, ectios 1.1-1.2
More informationChapter IV Integration Theory
Chapter IV Itegratio Theory Lectures 32-33 1. Costructio of the itegral I this sectio we costruct the abstract itegral. As a matter of termiology, we defie a measure space as beig a triple (, A, µ), where
More informationCommutativity in Permutation Groups
Commutativity i Permutatio Groups Richard Wito, PhD Abstract I the group Sym(S) of permutatios o a oempty set S, fixed poits ad trasiet poits are defied Prelimiary results o fixed ad trasiet poits are
More informationIntegrable Functions. { f n } is called a determining sequence for f. If f is integrable with respect to, then f d does exist as a finite real number
MATH 532 Itegrable Fuctios Dr. Neal, WKU We ow shall defie what it meas for a measurable fuctio to be itegrable, show that all itegral properties of simple fuctios still hold, ad the give some coditios
More informationMath 299 Supplement: Real Analysis Nov 2013
Math 299 Supplemet: Real Aalysis Nov 203 Algebra Axioms. I Real Aalysis, we work withi the axiomatic system of real umbers: the set R alog with the additio ad multiplicatio operatios +,, ad the iequality
More informationInfinite Sequences and Series
Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet
More informationSequences and Series
Sequeces ad Series Sequeces of real umbers. Real umber system We are familiar with atural umbers ad to some extet the ratioal umbers. While fidig roots of algebraic equatios we see that ratioal umbers
More informationChapter 6 Infinite Series
Chapter 6 Ifiite Series I the previous chapter we cosidered itegrals which were improper i the sese that the iterval of itegratio was ubouded. I this chapter we are goig to discuss a topic which is somewhat
More information} is said to be a Cauchy sequence provided the following condition is true.
Math 4200, Fial Exam Review I. Itroductio to Proofs 1. Prove the Pythagorea theorem. 2. Show that 43 is a irratioal umber. II. Itroductio to Logic 1. Costruct a truth table for the statemet ( p ad ~ r
More informationLecture 3 The Lebesgue Integral
Lecture 3: The Lebesgue Itegral 1 of 14 Course: Theory of Probability I Term: Fall 2013 Istructor: Gorda Zitkovic Lecture 3 The Lebesgue Itegral The costructio of the itegral Uless expressly specified
More information62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +
62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of
More information6.3 Testing Series With Positive Terms
6.3. TESTING SERIES WITH POSITIVE TERMS 307 6.3 Testig Series With Positive Terms 6.3. Review of what is kow up to ow I theory, testig a series a i for covergece amouts to fidig the i= sequece of partial
More informationRelations Among Algebras
Itroductio to leee Algebra Lecture 6 CS786 Sprig 2004 February 9, 2004 Relatios Amog Algebras The otio of free algebra described i the previous lecture is a example of a more geeral pheomeo called adjuctio.
More informationAdvanced Stochastic Processes.
Advaced Stochastic Processes. David Gamarik LECTURE 2 Radom variables ad measurable fuctios. Strog Law of Large Numbers (SLLN). Scary stuff cotiued... Outlie of Lecture Radom variables ad measurable fuctios.
More informationSOLVED EXAMPLES
Prelimiaries Chapter PELIMINAIES Cocept of Divisibility: A o-zero iteger t is said to be a divisor of a iteger s if there is a iteger u such that s tu I this case we write t s (i) 6 as ca be writte as
More informationMath 680 Fall Chebyshev s Estimates. Here we will prove Chebyshev s estimates for the prime counting function π(x). These estimates are
Math 680 Fall 07 Chebyshev s Estimates Here we will prove Chebyshev s estimates for the prime coutig fuctio. These estimates are superseded by the Prime Number Theorem, of course, but are iterestig from
More informationAn Introduction to Randomized Algorithms
A Itroductio to Radomized Algorithms The focus of this lecture is to study a radomized algorithm for quick sort, aalyze it usig probabilistic recurrece relatios, ad also provide more geeral tools for aalysis
More informationlim za n n = z lim a n n.
Lecture 6 Sequeces ad Series Defiitio 1 By a sequece i a set A, we mea a mappig f : N A. It is customary to deote a sequece f by {s } where, s := f(). A sequece {z } of (complex) umbers is said to be coverget
More informationCHAPTER I: Vector Spaces
CHAPTER I: Vector Spaces Sectio 1: Itroductio ad Examples This first chapter is largely a review of topics you probably saw i your liear algebra course. So why cover it? (1) Not everyoe remembers everythig
More informationSets and Probabilistic Models
ets ad Probabilistic Models Berli Che Departmet of Computer ciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Referece: - D. P. Bertsekas, J. N. Tsitsiklis, Itroductio to Probability, ectios 1.1-1.2
More information3. Sequences. 3.1 Basic definitions
3. Sequeces 3.1 Basic defiitios Defiitio 3.1 A (ifiite) sequece is a fuctio from the aturals to the real umbers. That is, it is a assigmet of a real umber to every atural umber. Commet 3.1 This is the
More information1.2 AXIOMATIC APPROACH TO PROBABILITY AND PROPERTIES OF PROBABILITY MEASURE 1.2 AXIOMATIC APPROACH TO PROBABILITY AND
NTEL- robability ad Distributios MODULE 1 ROBABILITY LECTURE 2 Topics 1.2 AXIOMATIC AROACH TO ROBABILITY AND ROERTIES OF ROBABILITY MEASURE 1.2.1 Iclusio-Exclusio Forula I the followig sectio we will discuss
More informationMath 4107: Abstract Algebra I Fall Webwork Assignment1-Groups (5 parts/problems) Solutions are on Webwork.
Math 4107: Abstract Algebra I Fall 2017 Assigmet 1 Solutios 1. Webwork Assigmet1-Groups 5 parts/problems) Solutios are o Webwork. 2. Webwork Assigmet1-Subgroups 5 parts/problems) Solutios are o Webwork.
More informationA gentle introduction to Measure Theory
A getle itroductio to Measure Theory Gaurav Chadalia Departmet of Computer ciece ad Egieerig UNY - Uiversity at Buffalo, Buffalo, NY gsc4@buffalo.edu March 12, 2007 Abstract This ote itroduces the basic
More informationSets. Sets. Operations on Sets Laws of Algebra of Sets Cardinal Number of a Finite and Infinite Set. Representation of Sets Power Set Venn Diagram
Sets MILESTONE Sets Represetatio of Sets Power Set Ve Diagram Operatios o Sets Laws of lgebra of Sets ardial Number of a Fiite ad Ifiite Set I Mathematical laguage all livig ad o-livig thigs i uiverse
More informationUnit 6: Sequences and Series
AMHS Hoors Algebra 2 - Uit 6 Uit 6: Sequeces ad Series 26 Sequeces Defiitio: A sequece is a ordered list of umbers ad is formally defied as a fuctio whose domai is the set of positive itegers. It is commo
More informationf n (x) f m (x) < ɛ/3 for all x A. By continuity of f n and f m we can find δ > 0 such that d(x, x 0 ) < δ implies that
Lecture 15 We have see that a sequece of cotiuous fuctios which is uiformly coverget produces a limit fuctio which is also cotiuous. We shall stregthe this result ow. Theorem 1 Let f : X R or (C) be a
More informationSequences A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence
Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece 1, 1, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet
More information3 Gauss map and continued fractions
ICTP, Trieste, July 08 Gauss map ad cotiued fractios I this lecture we will itroduce the Gauss map, which is very importat for its coectio with cotiued fractios i umber theory. The Gauss map G : [0, ]
More informationFind a formula for the exponential function whose graph is given , 1 2,16 1, 6
Math 4 Activity (Due by EOC Apr. ) Graph the followig epoetial fuctios by modifyig the graph of f. Fid the rage of each fuctio.. g. g. g 4. g. g 6. g Fid a formula for the epoetial fuctio whose graph is
More informationMAT1026 Calculus II Basic Convergence Tests for Series
MAT026 Calculus II Basic Covergece Tests for Series Egi MERMUT 202.03.08 Dokuz Eylül Uiversity Faculty of Sciece Departmet of Mathematics İzmir/TURKEY Cotets Mootoe Covergece Theorem 2 2 Series of Real
More informationA sequence of numbers is a function whose domain is the positive integers. We can see that the sequence
Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece,, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet as
More informationMa 530 Introduction to Power Series
Ma 530 Itroductio to Power Series Please ote that there is material o power series at Visual Calculus. Some of this material was used as part of the presetatio of the topics that follow. What is a Power
More informationCardinality Homework Solutions
Cardiality Homework Solutios April 16, 014 Problem 1. I the followig problems, fid a bijectio from A to B (you eed ot prove that the fuctio you list is a bijectio): (a) A = ( 3, 3), B = (7, 1). (b) A =
More informationLecture Notes for CS 313H, Fall 2011
Lecture Notes for CS 313H, Fall 011 August 5. We start by examiig triagular umbers: T () = 1 + + + ( = 0, 1,,...). Triagular umbers ca be also defied recursively: T (0) = 0, T ( + 1) = T () + + 1, or usig
More informationFUNDAMENTALS OF REAL ANALYSIS by
FUNDAMENTALS OF REAL ANALYSIS by Doğa Çömez Backgroud: All of Math 450/1 material. Namely: basic set theory, relatios ad PMI, structure of N, Z, Q ad R, basic properties of (cotiuous ad differetiable)
More information4 The Sperner property.
4 The Sperer property. I this sectio we cosider a surprisig applicatio of certai adjacecy matrices to some problems i extremal set theory. A importat role will also be played by fiite groups. I geeral,
More informationMath 155 (Lecture 3)
Math 55 (Lecture 3) September 8, I this lecture, we ll cosider the aswer to oe of the most basic coutig problems i combiatorics Questio How may ways are there to choose a -elemet subset of the set {,,,
More informationMA131 - Analysis 1. Workbook 3 Sequences II
MA3 - Aalysis Workbook 3 Sequeces II Autum 2004 Cotets 2.8 Coverget Sequeces........................ 2.9 Algebra of Limits......................... 2 2.0 Further Useful Results........................
More informationMAS111 Convergence and Continuity
MAS Covergece ad Cotiuity Key Objectives At the ed of the course, studets should kow the followig topics ad be able to apply the basic priciples ad theorems therei to solvig various problems cocerig covergece
More informationMath 140A Elementary Analysis Homework Questions 3-1
Math 0A Elemetary Aalysis Homework Questios -.9 Limits Theorems for Sequeces Suppose that lim x =, lim y = 7 ad that all y are o-zero. Detarime the followig limits: (a) lim(x + y ) (b) lim y x y Let s
More information5 Birkhoff s Ergodic Theorem
5 Birkhoff s Ergodic Theorem Amog the most useful of the various geeralizatios of KolmogorovâĂŹs strog law of large umbers are the ergodic theorems of Birkhoff ad Kigma, which exted the validity of the
More informationCIS Spring 2018 (instructor Val Tannen)
CIS 160 - Sprig 2018 (istructor Val Tae) Lecture 5 Thursday, Jauary 25 COUNTING We cotiue studyig how to use combiatios ad what are their properties. Example 5.1 How may 8-letter strigs ca be costructed
More informationMathematical Induction
Mathematical Iductio Itroductio Mathematical iductio, or just iductio, is a proof techique. Suppose that for every atural umber, P() is a statemet. We wish to show that all statemets P() are true. I a
More informationsubcaptionfont+=small,labelformat=parens,labelsep=space,skip=6pt,list=0,hypcap=0 subcaption ALGEBRAIC COMBINATORICS LECTURE 8 TUESDAY, 2/16/2016
subcaptiofot+=small,labelformat=pares,labelsep=space,skip=6pt,list=0,hypcap=0 subcaptio ALGEBRAIC COMBINATORICS LECTURE 8 TUESDAY, /6/06. Self-cojugate Partitios Recall that, give a partitio λ, we may
More informationDefinition 4.2. (a) A sequence {x n } in a Banach space X is a basis for X if. unique scalars a n (x) such that x = n. a n (x) x n. (4.
4. BASES I BAACH SPACES 39 4. BASES I BAACH SPACES Sice a Baach space X is a vector space, it must possess a Hamel, or vector space, basis, i.e., a subset {x γ } γ Γ whose fiite liear spa is all of X ad
More informationReal Numbers R ) - LUB(B) may or may not belong to B. (Ex; B= { y: y = 1 x, - Note that A B LUB( A) LUB( B)
Real Numbers The least upper boud - Let B be ay subset of R B is bouded above if there is a k R such that x k for all x B - A real umber, k R is a uique least upper boud of B, ie k = LUB(B), if () k is
More informationIn number theory we will generally be working with integers, though occasionally fractions and irrationals will come into play.
Number Theory Math 5840 otes. Sectio 1: Axioms. I umber theory we will geerally be workig with itegers, though occasioally fractios ad irratioals will come ito play. Notatio: Z deotes the set of all itegers
More informationDANIELL AND RIEMANN INTEGRABILITY
DANIELL AND RIEMANN INTEGRABILITY ILEANA BUCUR We itroduce the otio of Riema itegrable fuctio with respect to a Daiell itegral ad prove the approximatio theorem of such fuctios by a mootoe sequece of Jorda
More informationProperties of Fuzzy Length on Fuzzy Set
Ope Access Library Joural 206, Volume 3, e3068 ISSN Olie: 2333-972 ISSN Prit: 2333-9705 Properties of Fuzzy Legth o Fuzzy Set Jehad R Kider, Jaafar Imra Mousa Departmet of Mathematics ad Computer Applicatios,
More informationSequence A sequence is a function whose domain of definition is the set of natural numbers.
Chapter Sequeces Course Title: Real Aalysis Course Code: MTH3 Course istructor: Dr Atiq ur Rehma Class: MSc-I Course URL: wwwmathcityorg/atiq/fa8-mth3 Sequeces form a importat compoet of Mathematical Aalysis
More informationReal Analysis Fall 2004 Take Home Test 1 SOLUTIONS. < ε. Hence lim
Real Aalysis Fall 004 Take Home Test SOLUTIONS. Use the defiitio of a limit to show that (a) lim si = 0 (b) Proof. Let ε > 0 be give. Defie N >, where N is a positive iteger. The for ε > N, si 0 < si
More informationTEACHER CERTIFICATION STUDY GUIDE
COMPETENCY 1. ALGEBRA SKILL 1.1 1.1a. ALGEBRAIC STRUCTURES Kow why the real ad complex umbers are each a field, ad that particular rigs are ot fields (e.g., itegers, polyomial rigs, matrix rigs) Algebra
More informationThe Theory of Measures and Integration
The Theory of Measures ad Itegratio A Solio Maual for Vestrup (2003) Jiafei She School of Ecoomics, The Uiversity of New Soh Wales Sydey, Australia I hear, I forget; I see, I remember; I do, I uderstad.
More informationMath 525: Lecture 5. January 18, 2018
Math 525: Lecture 5 Jauary 18, 2018 1 Series (review) Defiitio 1.1. A sequece (a ) R coverges to a poit L R (writte a L or lim a = L) if for each ǫ > 0, we ca fid N such that a L < ǫ for all N. If the
More information(for homogeneous primes P ) defining global complex algebraic geometry. Definition: (a) A subset V CP n is algebraic if there is a homogeneous
Math 6130 Notes. Fall 2002. 4. Projective Varieties ad their Sheaves of Regular Fuctios. These are the geometric objects associated to the graded domais: C[x 0,x 1,..., x ]/P (for homogeeous primes P )
More informationSequences. A Sequence is a list of numbers written in order.
Sequeces A Sequece is a list of umbers writte i order. {a, a 2, a 3,... } The sequece may be ifiite. The th term of the sequece is the th umber o the list. O the list above a = st term, a 2 = 2 d term,
More informationn=1 a n is the sequence (s n ) n 1 n=1 a n converges to s. We write a n = s, n=1 n=1 a n
Series. Defiitios ad first properties A series is a ifiite sum a + a + a +..., deoted i short by a. The sequece of partial sums of the series a is the sequece s ) defied by s = a k = a +... + a,. k= Defiitio
More informationMath 220A Fall 2007 Homework #2. Will Garner A
Math 0A Fall 007 Homewor # Will Garer Pg 3 #: Show that {cis : a o-egative iteger} is dese i T = {z œ : z = }. For which values of q is {cis(q): a o-egative iteger} dese i T? To show that {cis : a o-egative
More informationA COUNTABLE SPACE WITH AN UNCOUNTABLE FUNDAMENTAL GROUP
A COUNTABLE SPACE WITH AN UNCOUNTABLE FUNDAMENTAL GROUP JEREMY BRAZAS AND LUIS MATOS Abstract. Traditioal examples of spaces that have ucoutable fudametal group (such as the Hawaiia earrig space) are path-coected
More informationLimit superior and limit inferior c Prof. Philip Pennance 1 -Draft: April 17, 2017
Limit erior ad limit iferior c Prof. Philip Peace -Draft: April 7, 207. Defiitio. The limit erior of a sequece a is the exteded real umber defied by lim a = lim a k k Similarly, the limit iferior of a
More informationSets are collection of objects that can be displayed in different forms. Two of these forms are called Roster Method and Builder Set Notation.
Sectio 2.1 Set ad Set Operators Defiitio of a set set is a collectio of objects thigs or umbers. Sets are collectio of objects that ca be displayed i differet forms. Two of these forms are called Roster
More informationIt is often useful to approximate complicated functions using simpler ones. We consider the task of approximating a function by a polynomial.
Taylor Polyomials ad Taylor Series It is ofte useful to approximate complicated fuctios usig simpler oes We cosider the task of approximatig a fuctio by a polyomial If f is at least -times differetiable
More informationWeek 5-6: The Binomial Coefficients
Wee 5-6: The Biomial Coefficiets March 6, 2018 1 Pascal Formula Theorem 11 (Pascal s Formula For itegers ad such that 1, ( ( ( 1 1 + 1 The umbers ( 2 ( 1 2 ( 2 are triagle umbers, that is, The petago umbers
More information6 Integers Modulo n. integer k can be written as k = qn + r, with q,r, 0 r b. So any integer.
6 Itegers Modulo I Example 2.3(e), we have defied the cogruece of two itegers a,b with respect to a modulus. Let us recall that a b (mod ) meas a b. We have proved that cogruece is a equivalece relatio
More informationPart A, for both Section 200 and Section 501
Istructios Please write your solutios o your ow paper. These problems should be treated as essay questios. A problem that says give a example or determie requires a supportig explaatio. I all problems,
More informationABOUT CHAOS AND SENSITIVITY IN TOPOLOGICAL DYNAMICS
ABOUT CHAOS AND SENSITIVITY IN TOPOLOGICAL DYNAMICS EDUARD KONTOROVICH Abstract. I this work we uify ad geeralize some results about chaos ad sesitivity. Date: March 1, 005. 1 1. Symbolic Dyamics Defiitio
More informationNOTES ON PROBABILITY THEORY FOR ENEE 620. Adrian Papamarcou. (with references to Probability and Measure by Patrick Billingsley)
NOTES ON PROBABILITY THEORY FOR ENEE 620 Adria Papamarcou (with refereces to Probability ad Measure by Patrick Billigsley) Revised February 2006 0 1. Itroductio to radom processes A radom process is a
More information1 Convergence in Probability and the Weak Law of Large Numbers
36-752 Advaced Probability Overview Sprig 2018 8. Covergece Cocepts: i Probability, i L p ad Almost Surely Istructor: Alessadro Rialdo Associated readig: Sec 2.4, 2.5, ad 4.11 of Ash ad Doléas-Dade; Sec
More informationPRELIM PROBLEM SOLUTIONS
PRELIM PROBLEM SOLUTIONS THE GRAD STUDENTS + KEN Cotets. Complex Aalysis Practice Problems 2. 2. Real Aalysis Practice Problems 2. 4 3. Algebra Practice Problems 2. 8. Complex Aalysis Practice Problems
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS
MASSACHUSTTS INSTITUT OF TCHNOLOGY 6.436J/5.085J Fall 2008 Lecture 9 /7/2008 LAWS OF LARG NUMBRS II Cotets. The strog law of large umbers 2. The Cheroff boud TH STRONG LAW OF LARG NUMBRS While the weak
More information