Ergodic Theory: Nonsingular Transformations
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Measure-Theoretic Probability I
Measure-Theoretic Probability I Steven P.Lalley Winter 2017 1 1 Measure Theory 1.1 Why Measure Theory? There are two different views – not necessarily exclusive – on what “probability” means: the subjectivist view and the frequentist view. To the subjectivist, probability is a system of laws that should govern a rational person’s behavior in situations where a bet must be placed (not necessarily just in a casino, but in situations where a decision must be made about how to proceed when only imperfect information about the outcome of the decision is available, for instance, should I allow Dr. Scissorhands to replace my arthritic knee by a plastic joint?). To the frequentist, the laws of probability describe the long- run relative frequencies of different events in “experiments” that can be repeated under roughly identical conditions, for instance, rolling a pair of dice. For the frequentist inter- pretation, it is imperative that probability spaces be large enough to allow a description of an experiment, like dice-rolling, that is repeated infinitely many times, and that the mathematical laws should permit easy handling of limits, so that one can make sense of things like “the probability that the long-run fraction of dice rolls where the two dice sum to 7 is 1/6”. But even for the subjectivist, the laws of probability should allow for description of situations where there might be a continuum of possible outcomes, or pos- sible actions to be taken. Once one is reconciled to the need for such flexibility, it soon becomes apparent that measure theory (the theory of countably additive, as opposed to merely finitely additive measures) is the only way to go. -
Integral Representation of a Linear Functional on Function Spaces
INTEGRAL REPRESENTATION OF LINEAR FUNCTIONALS ON FUNCTION SPACES MEHDI GHASEMI Abstract. Let A be a vector space of real valued functions on a non-empty set X and L : A −! R a linear functional. Given a σ-algebra A, of subsets of X, we present a necessary condition for L to be representable as an integral with respect to a measure µ on X such that elements of A are µ-measurable. This general result then is applied to the case where X carries a topological structure and A is a family of continuous functions and naturally A is the Borel structure of X. As an application, short solutions for the full and truncated K-moment problem are presented. An analogue of Riesz{Markov{Kakutani representation theorem is given where Cc(X) is replaced with whole C(X). Then we consider the case where A only consists of bounded functions and hence is equipped with sup-norm. 1. Introduction A positive linear functional on a function space A ⊆ RX is a linear map L : A −! R which assigns a non-negative real number to every function f 2 A that is globally non-negative over X. The celebrated Riesz{Markov{Kakutani repre- sentation theorem states that every positive functional on the space of continuous compactly supported functions over a locally compact Hausdorff space X, admits an integral representation with respect to a regular Borel measure on X. In symbols R L(f) = X f dµ, for all f 2 Cc(X). Riesz's original result [12] was proved in 1909, for the unit interval [0; 1]. -
Complex Measures 1 11
Tutorial 11: Complex Measures 1 11. Complex Measures In the following, (Ω, F) denotes an arbitrary measurable space. Definition 90 Let (an)n≥1 be a sequence of complex numbers. We a say that ( n)n≥1 has the permutation property if and only if, for ∗ ∗ +∞ 1 all bijections σ : N → N ,theseries k=1 aσ(k) converges in C Exercise 1. Let (an)n≥1 be a sequence of complex numbers. 1. Show that if (an)n≥1 has the permutation property, then the same is true of (Re(an))n≥1 and (Im(an))n≥1. +∞ 2. Suppose an ∈ R for all n ≥ 1. Show that if k=1 ak converges: +∞ +∞ +∞ + − |ak| =+∞⇒ ak = ak =+∞ k=1 k=1 k=1 1which excludes ±∞ as limit. www.probability.net Tutorial 11: Complex Measures 2 Exercise 2. Let (an)n≥1 be a sequence in R, such that the series +∞ +∞ k=1 ak converges, and k=1 |ak| =+∞.LetA>0. We define: + − N = {k ≥ 1:ak ≥ 0} ,N = {k ≥ 1:ak < 0} 1. Show that N + and N − are infinite. 2. Let φ+ : N∗ → N + and φ− : N∗ → N − be two bijections. Show the existence of k1 ≥ 1 such that: k1 aφ+(k) ≥ A k=1 3. Show the existence of an increasing sequence (kp)p≥1 such that: kp aφ+(k) ≥ A k=kp−1+1 www.probability.net Tutorial 11: Complex Measures 3 for all p ≥ 1, where k0 =0. 4. Consider the permutation σ : N∗ → N∗ defined informally by: φ− ,φ+ ,...,φ+ k ,φ− ,φ+ k ,...,φ+ k ,.. -
Appendix A. Measure and Integration
Appendix A. Measure and integration We suppose the reader is familiar with the basic facts concerning set theory and integration as they are presented in the introductory course of analysis. In this appendix, we review them briefly, and add some more which we shall need in the text. Basic references for proofs and a detailed exposition are, e.g., [[ H a l 1 ]] , [[ J a r 1 , 2 ]] , [[ K F 1 , 2 ]] , [[ L i L ]] , [[ R u 1 ]] , or any other textbook on analysis you might prefer. A.1 Sets, mappings, relations A set is a collection of objects called elements. The symbol card X denotes the cardi- nality of the set X. The subset M consisting of the elements of X which satisfy the conditions P1(x),...,Pn(x) is usually written as M = { x ∈ X : P1(x),...,Pn(x) }.A set whose elements are certain sets is called a system or family of these sets; the family of all subsystems of a given X is denoted as 2X . The operations of union, intersection, and set difference are introduced in the standard way; the first two of these are commutative, associative, and mutually distributive. In a { } system Mα of any cardinality, the de Morgan relations , X \ Mα = (X \ Mα)and X \ Mα = (X \ Mα), α α α α are valid. Another elementary property is the following: for any family {Mn} ,whichis { } at most countable, there is a disjoint family Nn of the same cardinality such that ⊂ \ ∪ \ Nn Mn and n Nn = n Mn.Theset(M N) (N M) is called the symmetric difference of the sets M,N and denoted as M #N. -
Descriptive Set Theory of Complete Quasi-Metric Spaces
数理解析研究所講究録 第 1790 巻 2012 年 16-30 16 Descriptive set theory of complete quasi-metric spaces Matthew de Brecht National Institute of Information and Communications Technology Kyoto, Japan Abstract We give a summary of results from our investigation into extending classical descriptive set theory to the entire class of countably based $T_{0}$ -spaces. Polish spaces play a central role in the descriptive set theory of metrizable spaces, and we suggest that countably based completely quasi-metrizable spaces, which we refer to as quasi-Polish spaces, play the central role in the extended theory. The class of quasi-Polish spaces is general enough to include both Polish spaces and $\omega$ -continuous domains, which have many applications in theoretical computer science. We show that quasi-Polish spaces are a very natural generalization of Polish spaces in terms of their topological characterizations and their complete- ness properties. In particular, a metrizable space is quasi-Polish if and only if it is Polish, and many classical theorems concerning Polish spaces, such as the Hausdorff-Kuratowski theorem, generalize to all quasi-Polish spaces. 1. Introduction Descriptive set theory has proven to be an invaluable tool for the study of separable metrizable spaces, and the techniques and results have been applied to many fields such as functional analysis, topological group theory, and math- ematical logic. Separable completely metrizable spaces, called Polish spaces, play a central role in classical descriptive set theory. These spaces include the space of natural numbers with the discrete topology, the real numbers with the Euclidean topology, as well as the separable Hilbert and Banach spaces. -
Algebra I Chapter 1. Basic Facts from Set Theory 1.1 Glossary of Abbreviations
Notes: c F.P. Greenleaf, 2000-2014 v43-s14sets.tex (version 1/1/2014) Algebra I Chapter 1. Basic Facts from Set Theory 1.1 Glossary of abbreviations. Below we list some standard math symbols that will be used as shorthand abbreviations throughout this course. means “for all; for every” • ∀ means “there exists (at least one)” • ∃ ! means “there exists exactly one” • ∃ s.t. means “such that” • = means “implies” • ⇒ means “if and only if” • ⇐⇒ x A means “the point x belongs to a set A;” x / A means “x is not in A” • ∈ ∈ N denotes the set of natural numbers (counting numbers) 1, 2, 3, • · · · Z denotes the set of all integers (positive, negative or zero) • Q denotes the set of rational numbers • R denotes the set of real numbers • C denotes the set of complex numbers • x A : P (x) If A is a set, this denotes the subset of elements x in A such that •statement { ∈ P (x)} is true. As examples of the last notation for specifying subsets: x R : x2 +1 2 = ( , 1] [1, ) { ∈ ≥ } −∞ − ∪ ∞ x R : x2 +1=0 = { ∈ } ∅ z C : z2 +1=0 = +i, i where i = √ 1 { ∈ } { − } − 1.2 Basic facts from set theory. Next we review the basic definitions and notations of set theory, which will be used throughout our discussions of algebra. denotes the empty set, the set with nothing in it • ∅ x A means that the point x belongs to a set A, or that x is an element of A. • ∈ A B means A is a subset of B – i.e. -
Ergodicity, Decisions, and Partial Information
Ergodicity, Decisions, and Partial Information Ramon van Handel Abstract In the simplest sequential decision problem for an ergodic stochastic pro- cess X, at each time n a decision un is made as a function of past observations X0,...,Xn 1, and a loss l(un,Xn) is incurred. In this setting, it is known that one may choose− (under a mild integrability assumption) a decision strategy whose path- wise time-average loss is asymptotically smaller than that of any other strategy. The corresponding problem in the case of partial information proves to be much more delicate, however: if the process X is not observable, but decisions must be based on the observation of a different process Y, the existence of pathwise optimal strategies is not guaranteed. The aim of this paper is to exhibit connections between pathwise optimal strategies and notions from ergodic theory. The sequential decision problem is developed in the general setting of an ergodic dynamical system (Ω,B,P,T) with partial information Y B. The existence of pathwise optimal strategies grounded in ⊆ two basic properties: the conditional ergodic theory of the dynamical system, and the complexity of the loss function. When the loss function is not too complex, a gen- eral sufficient condition for the existence of pathwise optimal strategies is that the dynamical system is a conditional K-automorphism relative to the past observations n n 0 T Y. If the conditional ergodicity assumption is strengthened, the complexity assumption≥ can be weakened. Several examples demonstrate the interplay between complexity and ergodicity, which does not arise in the case of full information. -
Topology and Descriptive Set Theory
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector TOPOLOGY AND ITS APPLICATIONS ELSEVIER Topology and its Applications 58 (1994) 195-222 Topology and descriptive set theory Alexander S. Kechris ’ Department of Mathematics, California Institute of Technology, Pasadena, CA 91125, USA Received 28 March 1994 Abstract This paper consists essentially of the text of a series of four lectures given by the author in the Summer Conference on General Topology and Applications, Amsterdam, August 1994. Instead of attempting to give a general survey of the interrelationships between the two subjects mentioned in the title, which would be an enormous and hopeless task, we chose to illustrate them in a specific context, that of the study of Bore1 actions of Polish groups and Bore1 equivalence relations. This is a rapidly growing area of research of much current interest, which has interesting connections not only with topology and set theory (which are emphasized here), but also to ergodic theory, group representations, operator algebras and logic (particularly model theory and recursion theory). There are four parts, corresponding roughly to each one of the lectures. The first contains a brief review of some fundamental facts from descriptive set theory. In the second we discuss Polish groups, and in the third the basic theory of their Bore1 actions. The last part concentrates on Bore1 equivalence relations. The exposition is essentially self-contained, but proofs, when included at all, are often given in the barest outline. Keywords: Polish spaces; Bore1 sets; Analytic sets; Polish groups; Bore1 actions; Bore1 equivalence relations 1. -
On the Continuing Relevance of Mandelbrot's Non-Ergodic Fractional Renewal Models of 1963 to 1967
Nicholas W. Watkins On the continuing relevance of Mandelbrot's non-ergodic fractional renewal models of 1963 to 1967 Article (Published version) (Refereed) Original citation: Watkins, Nicholas W. (2017) On the continuing relevance of Mandelbrot's non-ergodic fractional renewal models of 1963 to 1967.European Physical Journal B, 90 (241). ISSN 1434-6028 DOI: 10.1140/epjb/e2017-80357-3 Reuse of this item is permitted through licensing under the Creative Commons: © 2017 The Author CC BY 4.0 This version available at: http://eprints.lse.ac.uk/84858/ Available in LSE Research Online: February 2018 LSE has developed LSE Research Online so that users may access research output of the School. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. You may freely distribute the URL (http://eprints.lse.ac.uk) of the LSE Research Online website. Eur. Phys. J. B (2017) 90: 241 DOI: 10.1140/epjb/e2017-80357-3 THE EUROPEAN PHYSICAL JOURNAL B Regular Article On the continuing relevance of Mandelbrot's non-ergodic fractional renewal models of 1963 to 1967? Nicholas W. Watkins1,2,3 ,a 1 Centre for the Analysis of Time Series, London School of Economics and Political Science, London, UK 2 Centre for Fusion, Space and Astrophysics, University of Warwick, Coventry, UK 3 Faculty of Science, Technology, Engineering and Mathematics, Open University, Milton Keynes, UK Received 20 June 2017 / Received in final form 25 September 2017 Published online 11 December 2017 c The Author(s) 2017. This article is published with open access at Springerlink.com Abstract. -
Ergodic Theory
MATH41112/61112 Ergodic Theory Charles Walkden 4th January, 2018 MATH4/61112 Contents Contents 0 Preliminaries 2 1 An introduction to ergodic theory. Uniform distribution of real se- quences 4 2 More on uniform distribution mod 1. Measure spaces. 13 3 Lebesgue integration. Invariant measures 23 4 More examples of invariant measures 38 5 Ergodic measures: definition, criteria, and basic examples 43 6 Ergodic measures: Using the Hahn-Kolmogorov Extension Theorem to prove ergodicity 53 7 Continuous transformations on compact metric spaces 62 8 Ergodic measures for continuous transformations 72 9 Recurrence 83 10 Birkhoff’s Ergodic Theorem 89 11 Applications of Birkhoff’s Ergodic Theorem 99 12 Solutions to the Exercises 108 1 MATH4/61112 0. Preliminaries 0. Preliminaries 0.1 Contact details § The lecturer is Dr Charles Walkden, Room 2.241, Tel: 0161 275 5805, Email: [email protected]. My office hour is: Monday 2pm-3pm. If you want to see me at another time then please email me first to arrange a mutually convenient time. 0.2 Course structure § This is a reading course, supported by one lecture per week. I have split the notes into weekly sections. You are expected to have read through the material before the lecture, and then go over it again afterwards in your own time. In the lectures I will highlight the most important parts, explain the statements of the theorems and what they mean in practice, and point out common misunderstandings. As a general rule, I will not normally go through the proofs in great detail (but they are examinable unless indicated otherwise). -
Ergodicity and Metric Transitivity
Chapter 25 Ergodicity and Metric Transitivity Section 25.1 explains the ideas of ergodicity (roughly, there is only one invariant set of positive measure) and metric transivity (roughly, the system has a positive probability of going from any- where to anywhere), and why they are (almost) the same. Section 25.2 gives some examples of ergodic systems. Section 25.3 deduces some consequences of ergodicity, most im- portantly that time averages have deterministic limits ( 25.3.1), and an asymptotic approach to independence between even§ts at widely separated times ( 25.3.2), admittedly in a very weak sense. § 25.1 Metric Transitivity Definition 341 (Ergodic Systems, Processes, Measures and Transfor- mations) A dynamical system Ξ, , µ, T is ergodic, or an ergodic system or an ergodic process when µ(C) = 0 orXµ(C) = 1 for every T -invariant set C. µ is called a T -ergodic measure, and T is called a µ-ergodic transformation, or just an ergodic measure and ergodic transformation, respectively. Remark: Most authorities require a µ-ergodic transformation to also be measure-preserving for µ. But (Corollary 54) measure-preserving transforma- tions are necessarily stationary, and we want to minimize our stationarity as- sumptions. So what most books call “ergodic”, we have to qualify as “stationary and ergodic”. (Conversely, when other people talk about processes being “sta- tionary and ergodic”, they mean “stationary with only one ergodic component”; but of that, more later. Definition 342 (Metric Transitivity) A dynamical system is metrically tran- sitive, metrically indecomposable, or irreducible when, for any two sets A, B n ∈ , if µ(A), µ(B) > 0, there exists an n such that µ(T − A B) > 0. -
6.436J Lecture 13: Product Measure and Fubini's Theorem
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 13 10/22/2008 PRODUCT MEASURE AND FUBINI'S THEOREM Contents 1. Product measure 2. Fubini’s theorem In elementary math and calculus, we often interchange the order of summa tion and integration. The discussion here is concerned with conditions under which this is legitimate. 1 PRODUCT MEASURE Consider two probabilistic experiments described by probability spaces (�1; F1; P1) and (�2; F2; P2), respectively. We are interested in forming a probabilistic model of a “joint experiment” in which the original two experiments are car ried out independently. 1.1 The sample space of the joint experiment If the first experiment has an outcome !1, and the second has an outcome !2, then the outcome of the joint experiment is the pair (!1; !2). This leads us to define a new sample space � = �1 × �2. 1.2 The �-field of the joint experiment Next, we need a �-field on �. If A1 2 F1, we certainly want to be able to talk about the event f!1 2 A1g and its probability. In terms of the joint experiment, this would be the same as the event A1 × �1 = f(!1; !2) j !1 2 A1; !2 2 �2g: 1 Thus, we would like our �-field on � to include all sets of the form A1 × �2, (with A1 2 F1) and by symmetry, all sets of the form �1 ×A2 (with (A2 2 F2). This leads us to the following definition. Definition 1. We define F1 ×F2 as the smallest �-field of subsets of �1 ×�2 that contains all sets of the form A1 × �2 and �1 × A2, where A1 2 F1 and A2 2 F2.