2.1.3 Outer Measures and Construction of Measures Recall the Construction of Lebesgue Measure on the Real Line
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Measure Theory and Probability Theory
Measure Theory and Probability Theory Stéphane Dupraz In this chapter, we aim at building a theory of probabilities that extends to any set the theory of probability we have for finite sets (with which you are assumed to be familiar). For a finite set with N elements Ω = {ω1, ..., ωN }, a probability P takes any n positive numbers p1, ..., pN that sum to one, and attributes to any subset S of Ω the number (S) = P p . Extending this definition to infinitely countable sets such as P i/ωi∈S i N poses no difficulty: we can in the same way assign a positive number to each integer n ∈ N and require that P∞ 1 P n=1 pn = 1. We can then define the probability of a subset S ⊆ N as P(S) = n∈S pn. Things get more complicated when we move to uncountable sets such as the real line R. To be sure, it is possible to assign a positive number to each real number. But how to get from these positive numbers to the probability of any subset of R?2 To get a definition of a probability that applies without a hitch to uncountable sets, we give in the strategy we used for finite and countable sets and start from scratch. The definition of a probability we are going to use was borrowed from measure theory by Kolmogorov in 1933, which explains the title of this chapter. What do probabilities have to do with measurement? Simple: assigning a probability to an event is measuring the likeliness of this event. -
Results Real Analysis I and II, MATH 5453-5463, 2006-2007
Main results Real Analysis I and II, MATH 5453-5463, 2006-2007 Section Homework Introduction. 1.3 Operations with sets. DeMorgan Laws. 1.4 Proposition 1. Existence of the smallest algebra containing C. 2.5 Open and closed sets. 2.6 Continuous functions. Proposition 18. Hw #1. p.16 #9, 11, 17, 18; p.19 #19. 2.7 Borel sets. p.49 #40, 42, 43; p.53 #53*. 3.2 Outer measure. Proposition 1. Outer measure of an interval. Proposition 2. Subadditivity of the outer measure. Proposition 5. Approximation by open sets. 3.3 Measurable sets. Lemma 6. Measurability of sets of outer measure zero. Lemma 7. Measurability of the union. Hw #2. p.55 #1-4; p.58 # 7, 8. Theorem 10. Measurable sets form a sigma-algebra. Lemma 11. Interval is measurable. Theorem 12. Borel sets are measurable. Proposition 13. Sigma additivity of the measure. Proposition 14. Continuity of the measure. Proposition 15. Approximation by open and closed sets. Hw #3. p.64 #9-11, 13, 14. 3.4 A nonmeasurable set. 3.5 Measurable functions. Proposition 18. Equivalent definitions of measurability. Proposition 19. Sums and products of measurable functions. Theorem 20. Infima and suprema of measurable functions. Hw #4. p.70 #18-22. 3.6 Littlewood's three principles. Egoroff's theorem. Lusin's theorem. 4.2 Prop.2. Lebesgue's integral of a simple function and its props. Lebesgue's integral of a bounded measurable function. Proposition 3. Criterion of integrability. Proposition 5. Properties of integrals of bounded functions. Proposition 6. Bounded convergence theorem. 4.3 Lebesgue integral of a nonnegative function and its properties. -
Hausdorff Measure
Hausdorff Measure Jimmy Briggs and Tim Tyree December 3, 2016 1 1 Introduction In this report, we explore the the measurement of arbitrary subsets of the metric space (X; ρ); a topological space X along with its distance function ρ. We introduce Hausdorff Measure as a natural way of assigning sizes to these sets, especially those of smaller \dimension" than X: After an exploration of the salient properties of Hausdorff Measure, we proceed to a definition of Hausdorff dimension, a separate idea of size that allows us a more robust comparison between rough subsets of X. Many of the theorems in this report will be summarized in a proof sketch or shown by visual example. For a more rigorous treatment of the same material, we redirect the reader to Gerald B. Folland's Real Analysis: Modern techniques and their applications. Chapter 11 of the 1999 edition served as our primary reference. 2 Hausdorff Measure 2.1 Measuring low-dimensional subsets of X The need for Hausdorff Measure arises from the need to know the size of lower-dimensional subsets of a metric space. This idea is not as exotic as it may sound. In a high school Geometry course, we learn formulas for objects of various dimension embedded in R3: In Figure 1 we see the line segment, the circle, and the sphere, each with it's own idea of size. We call these length, area, and volume, respectively. Figure 1: low-dimensional subsets of R3: 2 4 3 2r πr 3 πr Note that while only volume measures something of the same dimension as the host space, R3; length, and area can still be of interest to us, especially 2 in applications. -
Caratheodory's Extension Theorem
Caratheodory’s extension theorem DBW August 3, 2016 These notes are meant as introductory notes on Caratheodory’s extension theorem. The presentation is not completely my own work; the presentation heavily relies on the pre- sentation of Noel Vaillant on http://www.probability.net/WEBcaratheodory.pdf. To make the line of arguments as clear as possible, the starting point is the notion of a ring on a topological space and not the notion of a semi-ring. 1 Elementary definitions and properties We fix a topological space Ω. The power set of Ω is denoted P(Ω) and consists of all subsets of Ω. Definition 1. A ring on on Ω is a subset R of P(Ω), such that (i) ∅ ∈ R (ii) A, B ∈ R ⇒ A ∪ B ∈ R (iii) A, B ∈ R ⇒ A \ B ∈ R Definition 2. A σ-algebra on Ω is a subset Σ of P(Ω) such that (i) ∅ ∈ Σ (ii) (An)n∈N ∈ Σ ⇒ ∪n An ∈ Σ (iii) A ∈ Σ ⇒ Ac ∈ Σ Since A∩B = A\(A\B) it follows that any ring on Ω is closed under finite intersections; c c hence any ring is also a semi-ring. Since ∩nAn = (∪nA ) it follows that any σ-algebra is closed under arbitrary intersections. And from A \ B = A ∩ Bc we deduce that any σ-algebra is also a ring. If (Ri)i∈I is a set of rings on Ω then it is clear that ∩I Ri is also a ring on Ω. Let S be any subset of P(Ω), then we call the intersection of all rings on Ω containing S the ring generated by S. -
Construction of Geometric Outer-Measures and Dimension Theory
Construction of Geometric Outer-Measures and Dimension Theory by David Worth B.S., Mathematics, University of New Mexico, 2003 THESIS Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science Mathematics The University of New Mexico Albuquerque, New Mexico December, 2006 c 2008, David Worth iii DEDICATION To my lovely wife Meghan, to whom I am eternally grateful for her support and love. Without her I would never have followed my education or my bliss. iv ACKNOWLEDGMENTS I would like to thank my advisor, Dr. Terry Loring, for his years of encouragement and aid in pursuing mathematics. I would also like to thank Dr. Cristina Pereyra for her unfailing encouragement and her aid in completing this daunting task. I would like to thank Dr. Wojciech Kucharz for his guidance and encouragement for the entirety of my adult mathematical career. Moreover I would like to thank Dr. Jens Lorenz, Dr. Vladimir I Koltchinskii, Dr. James Ellison, and Dr. Alexandru Buium for their years of inspirational teaching and their influence in my life. v Construction of Geometric Outer-Measures and Dimension Theory by David Worth ABSTRACT OF THESIS Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science Mathematics The University of New Mexico Albuquerque, New Mexico December, 2006 Construction of Geometric Outer-Measures and Dimension Theory by David Worth B.S., Mathematics, University of New Mexico, 2003 M.S., Mathematics, University of New Mexico, 2008 Abstract Geometric Measure Theory is the rigorous mathematical study of the field commonly known as Fractal Geometry. In this work we survey means of constructing families of measures, via the so-called \Carath´eodory construction", which isolate certain small- scale features of complicated sets in a metric space. -
M.Sc. I Semester Mathematics (CBCS) 2015-16 Teaching Plan
M.Sc. I Semester Mathematics (CBCS) 2015-16 Teaching Plan Paper : M1 MAT 01-CT01 (ALGEBRA-I) Day Topic Reference Book Chapter UNIT-I 1-4 External and Internal direct product of Algebra (Agrawal, R.S.) Chapter 6 two and finite number of subgroups; 5-6 Commutator subgroup - do - Chapter 6 7-9 Cauchy’s theorem for finite abelian and - do - Chapter 6 non abelian groups. 10-11 Tutorials UNIT-II 12-15 Sylow’s three theorem and their easy - do - Chapter 6 applications 16-18 Subnormal and Composition series, - do - Chapter 6 19-21 Zassenhaus lemma and Jordan Holder - do - Chapter 6 theorem. 22-23 Tutorials - do - UNIT-III 24-26 Solvable groups and their properties Modern Algebra (Surjeet Chapter 5 Singh and Quazi Zameeruddin) 27-29 Nilpotent groups - do - Chapter 5 30-32 Fundamental theorem for finite abelian - do - Chapter 4 groups. 33-34 Tutorials UNIT-IV 35-38 Annihilators of subspace and its Algebra (Agrawal, R.S.) Chapter 12 dimension in finite dimensional vector space 39-41 Invariant, - do - Chapter 12 42-45 Projection, - do - Chapter 12 46-47 adjoins. - do - Chapter 12 48-49 Tutorials UNIT-V 50-54 Singular and nonsingular linear Algebra (Agrawal, R.S.) Chapter 12 transformation 55-58 quadratic forms and Diagonalization. - do - Chapter12 59-60 Tutorials - do - 61-65 Students Interaction & Difficulty solving - - M.Sc. I Semester Mathematics (CBCS) 2015-16 Teaching Plan Paper : M1 MAT 02-CT02 (REAL ANALYSIS) Day Topic Reference Book Chapter UNIT-I 1-2 Length of an interval, outer measure of a Theory and Problems of Chapter 2 subset of R, Labesgue outer measure of a Real Variables: Murray subset of R R. -
Section 17.5. the Carathéodry-Hahn Theorem: the Extension of A
17.5. The Carath´eodory-Hahn Theorem 1 Section 17.5. The Carath´eodry-Hahn Theorem: The Extension of a Premeasure to a Measure Note. We have µ defined on S and use µ to define µ∗ on X. We then restrict µ∗ to a subset of X on which µ∗ is a measure (denoted µ). Unlike with Lebesgue measure where the elements of S are measurable sets themselves (and S is the set of open intervals), we may not have µ defined on S. In this section, we look for conditions on µ : S → [0, ∞] which imply the measurability of the elements of S. Under these conditions, µ is then an extension of µ from S to M (the σ-algebra of measurable sets). ∞ Definition. A set function µ : S → [0, ∞] is finitely additive if whenever {Ek}k=1 ∞ is a finite disjoint collection of sets in S and ∪k=1Ek ∈ S, we have n n µ · Ek = µ(Ek). k=1 ! k=1 [ X Note. We have previously defined finitely monotone for set functions and Propo- sition 17.6 gives finite additivity for an outer measure on M, but this is the first time we have discussed a finitely additive set function. Proposition 17.11. Let S be a collection of subsets of X and µ : S → [0, ∞] a set function. In order that the Carath´eodory measure µ induced by µ be an extension of µ (that is, µ = µ on S) it is necessary that µ be both finitely additive and countably monotone and, if ∅ ∈ S, then µ(∅) = 0. -
Abelian Group, 521, 526 Absolute Value, 190 Accumulation, Point Of
Index Abelian group, 521, 526 A-set. SeeAnalytic set Absolutevalue, 190 Asymptoticallyequal. 479 Accumulation, point of, 196 Atlas , 231; of holomorphically related Adjoint differentialform, 157, 167 charts, 245 Adjoint operator, 403 Atomic theory, 415 Adjoint space, 397 Automorphism group, 510, 511 Algebra, 524; Boolean, 91, 92; Axiomatic method, in geometry, 507-508 fundamentaltheorem of, 195-196; homo logical, 519-520; normed, 516 BAIREclasses, 460; first, 460, 462, 463; Almost all, 479 of functions, 448 Almost continuous, 460 BAIREcondition, 464 Almost equal, 479 BAIREfunction, 464, 473; non-, 474 Almost everywhere, 70 BAIREspace, 464 Almost linear equation, 321, 323 BAIREsystem, of functions, 459, 460 Alternating differentialform, 185; BAIREtheorem, 448, 460, 462 differentialoperations for, 159-165; BANACH, S., 516 theory of, vi, 143 BANACHfixed point theorem, 423 Alternative theorem, 296, 413 BANACHspace, 338, 340, 393, 399, 432, Analysis, v, 1; axiomaticmethod in, 435,437, 516; adjoint, 400; 512-518; complex, vi ; functional, conjugate, 400; dual, 400; theory of, vi 391; harmonic, 518; and number BANACHtheorem, 446, 447 theory, 500-501 Band spectra, 418 Analytic function, definedby function BAYES theorem, 109 element, 242 BELTRAMIdifferential equation, 325 Analytic numbertheory, 480 BERNOULLI, DANIEL, 23 Analytic operation, 468 BERNOULLI, JACOB, 89, 360 Analytic set, 448, 458, 465, 468, 469; BERNOULLI, JOHANN, 23 linear, 466 BERNOULLIdistribution, 96 Angle-preservingtransformation, 194 BERNOULLIlaw, of large numbers, 116 a-points, -
2.5 Outer Measure and Measurable Sets. Note the Results of This Section Concern Any Given Outer Measure Λ
2.5 Outer Measure and Measurable sets. Note The results of this section concern any given outer measure ¸. If an outer measure ¸ on a set X were a measure then it would be additive. In particular, given any two sets A; B ⊆ X we have that A \ B and A \ Bc are disjoint with (A \ B) [ (A \ Bc) = A and so we would have ¸(A) = ¸(A \ B) + ¸(A \ Bc): We will see later that this does not necessarily hold for all A and B but it does lead to the following definition. Definition Let ¸ be an outer measure on a set X. Then E ⊆ X is said to be measurable with respect to ¸ (or ¸-measurable) if ¸(A) = ¸(A \ E) + ¸(A \ Ec) for all A ⊆ X. (7) (This can be read as saying that we take each and every possible “test set”, A, look at the measures of the parts of A that fall within and without E, and check whether these measures add up to that of A.) Since ¸ is subadditive we have ¸(A) · ¸(A\E)+¸(A\Ec) so, in checking measurability, we need only verify that ¸(A) ¸ ¸(A \ E) + ¸(A \ Ec) for all A ⊆ X. (8) Let M = M(¸) denote the collection of ¸-measurable sets. Theorem 2.6 M is a field. Proof Trivially Á and X are in M. Take any E1;E2 2 M and any test set A ⊆ X. Then c ¸(A) = ¸(A \ E1) + ¸(A \ E1): c Now apply the definition of measurability for E2 with the test set A \ E1 to get c c c c ¸(A \ E1) = ¸((A \ E1) \ E2) + ¸((A \ E1) \ E2) c c = ¸(A \ E1 \ E2) + ¸(A \ (E1 [ E2) ): Combining c c ¸(A) = ¸(A \ E1) + ¸(A \ E1 \ E2) + ¸(A \ (E1 [ E2) ): (9) 1 We hope to use the subadditivity of ¸ on the first two term on the right hand side of (9). -
PATHOLOGICAL 1. Introduction
PATHOLOGICAL APPLICATIONS OF LEBESGUE MEASURE TO THE CANTOR SET AND NON-INTEGRABLE DERIVATIVES PRICE HARDMAN WHITMAN COLLEGE 1. Introduction Pathological is an oft used word in the mathematical community, and in that context it has quite a different meaning than in everyday usage. In mathematics, something is said to be \pathological" if it is particularly poorly behaved or counterintuitive. However, this is not to say that mathe- maticians use this term in an entirely derisive manner. Counterintuitive and unexpected results often challenge common conventions and invite a reevalu- ation of previously accepted methods and theories. In this way, pathological examples can be seen as impetuses for mathematical progress. As a result, pathological examples crop up quite often in the history of mathematics, and we can usually learn a great deal about the history of a given field by studying notable pathological examples in that field. In addition to allowing us to weave historical narrative into an otherwise noncontextualized math- mematical discussion, these examples shed light on the boundaries of the subfield of mathematics in which we find them. It is the intention then of this piece to investigate the basic concepts, ap- plications, and historical development of measure theory through the use of pathological examples. We will develop some basic tools of Lebesgue mea- sure on the real line and use them to investigate some historically significant (and fascinating) examples. One such example is a function whose deriva- tive is bounded yet not Riemann integrable, seemingly in violation of the Fundamental Theorem of Calculus. In fact, this example played a large part in the development of the Lebesgue integral in the early years of the 20th century. -
Lecture Notes for Data and Uncertainty (First Part)
Lecture Notes for Data and Uncertainty (first part) Jochen Bröcker December 29, 2020 1 Introduction Sections 1 to 10 will cover probability theory, integration, and a bit of statis- tics (roughly in that order). The second part (Sect. 11-13) –not part of these notes– will explain an important technique in statistics called Monte Carlo simulations. This introduction will motivate probability theory and statistics a little bit, and in particular why we need concepts from measure theory and integration, which is often perceived as abstract and complicated. Probability It is not easy to explain what probability theory is about without sounding tautological. One might say that it allows to quantify uncertainty or chance, but then what do we mean by “uncertainty” or “chance”? De Moivre’s seminal textbook “The Doctrine of Chances” [dM67] is widely considered as the first textbook on probability theory, and the theory has undergone enormous developments since then. In particular, there is an ax- iomatic framework which has been universally adopted and which we will discuss in this course. But even though De Moivre’s book was first published in 1718, there is still some debate as to the interpretation of probability theory, or in other words, to what this nice axiomatic framework actually pertains. Different interpretations of probability have been put forward, but somewhat fortunately to the student, the differences matter little as far as the mathematics is concerned. Nonetheless, we will briefly mention the most prominent interpretations of probability -
Measure Theory
Appendix A Measure theory In this appendix we collect some important notions from measure theory. The goal is not to present a self-contained presentation, but rather to establish the basic definitions and theorems from the theory for reference in the main text. As such, the presentation omits certain existence theorems and many of the proofs of other theorems (although references are given). The focus is strongly on finite (e.g. probability-) measures, in places at the expense of generality. Some background in elementary set-theory and analysis is required. As a comprehensive reference, we note Kingman and Taylor (1966) [52], alternatives being Dudley (1989) [29] and Billingsley (1986) [15]. A.1 Sets and sigma-algebras Rough setup: set operations, monotony of sequences of subsets, set-limits, sigma-algebra’s, measurable spaces, set-functions, product spaces. Definition A.1.1. A measurable space (Ω, F ) consists of a set Ω and a σ-algebra F of subsets of Ω. A.2 Measures Rough setup: set-functions, (signed) measures, probability measures, sigma-additivity, sigma- finiteness Theorem A.2.1. Let (Ω, F ) be a measurable space with measure µ : F [0, ]. Then, → ∞ (i) for any monotone decreasing sequence (Fn)n 1 in F such that µ(Fn) < for some n, ≥ ∞ ∞ lim µ(Fn)=µ Fn , (A.1) n →∞ n=1 91 92 Measure theory (ii) for any monotone increasing sequence (Gn)n 1 in F , ≥ ∞ lim µ(Gn)=µ Gn , (A.2) n →∞ n=1 Theorem A.2.1) is sometimes referred to as the continuity theorem for measures, because if we view F as the monotone limit lim F , (A.1) can be read as lim µ(F )=µ(lim F ), ∩n n n n n n n expressing continuity from below.