Measure-Valued Differentiation for Finite Products of Measures : Theory and Applications
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Version of 21.8.15 Chapter 43 Topologies and Measures II The
Version of 21.8.15 Chapter 43 Topologies and measures II The first chapter of this volume was ‘general’ theory of topological measure spaces; I attempted to distinguish the most important properties a topological measure can have – inner regularity, τ-additivity – and describe their interactions at an abstract level. I now turn to rather more specialized investigations, looking for features which offer explanations of the behaviour of the most important spaces, radiating outwards from Lebesgue measure. In effect, this chapter consists of three distinguishable parts and two appendices. The first three sections are based on ideas from descriptive set theory, in particular Souslin’s operation (§431); the properties of this operation are the foundation for the theory of two classes of topological space of particular importance in measure theory, the K-analytic spaces (§432) and the analytic spaces (§433). The second part of the chapter, §§434-435, collects miscellaneous results on Borel and Baire measures, looking at the ways in which topological properties of a space determine properties of the measures it carries. In §436 I present the most important theorems on the representation of linear functionals by integrals; if you like, this is the inverse operation to the construction of integrals from measures in §122. The ideas continue into §437, where I discuss spaces of signed measures representing the duals of spaces of continuous functions, and topologies on spaces of measures. The first appendix, §438, looks at a special topic: the way in which the patterns in §§434-435 are affected if we assume that our spaces are not unreasonably complex in a rather special sense defined in terms of measures on discrete spaces. -
Weak Convergence of Probability Measures Revisited
WEAK CONVERGENCE OF PROBABILITY MEASURES REVISITED Cabriella saltnetti' Roger J-B wetse April 1987 W P-87-30 This research was supported in part by MPI, Projects: "Calcolo Stocastico e Sistemi Dinamici Statistici" and "Modelli Probabilistic" 1984, and by the National Science Foundation. Working Papers are interim reports on work of the lnternational Institute for Applied Systems Analysis and have received only limited review. Views or opinions expressed herein do not necessarily represent those of the Institute or of its National Member Organizations. INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSlS A-2361 Laxenburg, Austria FOREWORD The modeling of stochastic processes is a fundamental tool in the study of models in- volving uncertainty, a major topic at SDS. A number of classical convergence results (and extensions) for probability measures are derived by relying on new tools that are particularly useful in stochastic optimization and extremal statistics. Alexander B. Kurzhanski Chairman System and Decision Sciences Program ABSTRACT The hypo-convergence of upper semicontinuous functions provides a natural frame- work for the study of the convergence of probability measures. This approach also yields some further characterizations of weak convergence and tightness. CONTENTS 1 About Continuity and Measurability 2 Convergence of Sets and Semicontinuous Functions 3 SC-Measures and SGPrerneasures on 7 (E) 4 Hypo-Limits of SGMeasures and Tightness 5 Tightness and Equi-Semicontinuity Acknowledgment Appendix A Appendix B References - vii - WEAK CONVERGENCE OF PROBABILITY MEASURES REVISITED Gabriella salinettil and Roger J-B wetsL luniversiti "La Sapienza", 00185 Roma, Italy. 2~niversityof California, Davis, CA 95616. 1. ABOUT CONTINUITY AND MEASURABILITY A probabilistic structure - a space of possible events, a sigma-field of (observable) subcollections of events, and a probability measure defined on this sigma-field - does not have a built-in topological structure. -
Arxiv:1009.3824V2 [Math.OC] 7 Feb 2012 E Fpstv Nees Let Integers
OPTIMIZATION AND CONVERGENCE OF OBSERVATION CHANNELS IN STOCHASTIC CONTROL SERDAR YUKSEL¨ AND TAMAS´ LINDER Abstract. This paper studies the optimization of observation channels (stochastic kernels) in partially observed stochastic control problems. In particular, existence and continuity properties are investigated mostly (but not exclusively) concentrating on the single-stage case. Continuity properties of the optimal cost in channels are explored under total variation, setwise convergence, and weak convergence. Sufficient conditions for compactness of a class of channels under total variation and setwise convergence are presented and applications to quantization are explored. Key words. Stochastic control, information theory, observation channels, optimization, quan- tization AMS subject classifications. 15A15, 15A09, 15A23 1. Introduction. In stochastic control, one is often concerned with the following problem: Given a dynamical system, an observation channel (stochastic kernel), a cost function, and an action set, when does there exist an optimal policy, and what is an optimal control policy? The theory for such problems is advanced, and practically significant, spanning a wide variety of applications in engineering, economics, and natural sciences. In this paper, we are interested in a dual problem with the following questions to be explored: Given a dynamical system, a cost function, an action set, and a set of observation channels, does there exist an optimal observation channel? What is the right convergence notion for continuity in such observation channels for optimization purposes? The answers to these questions may provide useful tools for characterizing an optimal observation channel subject to constraints. We start with the probabilistic setup of the problem. Let X ⊂ Rn, be a Borel set in which elements of a controlled Markov process {Xt, t ∈ Z+} live. -
MARSTRAND's THEOREM and TANGENT MEASURES Contents 1
MARSTRAND'S THEOREM AND TANGENT MEASURES ALEKSANDER SKENDERI Abstract. This paper aims to prove and motivate Marstrand's theorem, which is a fundamental result in geometric measure theory. Along the way, we introduce and motivate the notion of tangent measures, while also proving several related results. The paper assumes familiarity with the rudiments of measure theory such as Lebesgue measure, Lebesgue integration, and the basic convergence theorems. Contents 1. Introduction 1 2. Preliminaries on Measure Theory 2 2.1. Weak Convergence of Measures 2 2.2. Differentiation of Measures and Covering Theorems 3 2.3. Hausdorff Measure and Densities 4 3. Marstrand's Theorem 5 3.1. Tangent measures and uniform measures 5 3.2. Proving Marstrand's Theorem 8 Acknowledgments 16 References 16 1. Introduction Some of the most important objects in all of mathematics are smooth manifolds. Indeed, they occupy a central position in differential geometry, algebraic topology, and are often very important when relating mathematics to physics. However, there are also many objects of interest which may not be differentiable over certain sets of points in their domains. Therefore, we want to study more general objects than smooth manifolds; these objects are known as rectifiable sets. The following definitions and theorems require the notion of Hausdorff measure. If the reader is unfamiliar with Hausdorff measure, he should consult definition 2.10 of section 2.3 before continuing to read this section. Definition 1.1. A k-dimensional Borel set E ⊂ Rn is called rectifiable if there 1 k exists a countable family fΓigi=1 of Lipschitz graphs such that H (E n [ Γi) = 0. -
Arxiv:2102.05840V2 [Math.PR]
SEQUENTIAL CONVERGENCE ON THE SPACE OF BOREL MEASURES LIANGANG MA Abstract We study equivalent descriptions of the vague, weak, setwise and total- variation (TV) convergence of sequences of Borel measures on metrizable and non-metrizable topological spaces in this work. On metrizable spaces, we give some equivalent conditions on the vague convergence of sequences of measures following Kallenberg, and some equivalent conditions on the TV convergence of sequences of measures following Feinberg-Kasyanov-Zgurovsky. There is usually some hierarchy structure on the equivalent descriptions of convergence in different modes, but not always. On non-metrizable spaces, we give examples to show that these conditions are seldom enough to guarantee any convergence of sequences of measures. There are some remarks on the attainability of the TV distance and more modes of sequential convergence at the end of the work. 1. Introduction Let X be a topological space with its Borel σ-algebra B. Consider the collection M˜ (X) of all the Borel measures on (X, B). When we consider the regularity of some mapping f : M˜ (X) → Y with Y being a topological space, some topology or even metric is necessary on the space M˜ (X) of Borel measures. Various notions of topology and metric grow out of arXiv:2102.05840v2 [math.PR] 28 Apr 2021 different situations on the space M˜ (X) in due course to deal with the corresponding concerns of regularity. In those topology and metric endowed on M˜ (X), it has been recognized that the vague, weak, setwise topology as well as the total-variation (TV) metric are highlighted notions on the topological and metric description of M˜ (X) in various circumstances, refer to [Kal, GR, Wul]. -
A Discussion on Analytical Study of Semi-Closed Set in Topological Space
The International journal of analytical and experimental modal analysis ISSN NO:0886-9367 A discussion on analytical study of Semi-closed set in topological space 1 Dr. Priti Kumari, 2 Sukesh Kumar Das, 3 Dr. Ranjana & 4 Rupesh Kumar 1 & 2 Guest Assistant Professor, Department of Mathematics Saharsa College of Engineering , Saharsa ( 852201 ), Bihar, INDIA 3 University Professor, University department of Mathematics Tilka Manjhi Bhagalpur University, Bhagalpur ( 812007 ), Bihar , INDIA 4 M. Sc., Department of Physics A. N. College Patna, Univ. of Patna ( 800013 ), Bihar, INDIA [email protected] , [email protected] , [email protected] & [email protected] Abstract : In this paper, we introduce a new class of sets in the topological space, namely Semi- closed sets in the topological space. We find characterizations of these sets. Further, we study some fundamental properties of Semi-closed sets in the topological space. Keywords : Open set, Closed set, Interior of a set & Closure of a set. I. Introduction The term Semi-closed set which is a weak form of closed set in a topological space and it is introduced and defined by the mathematician N. Biswas [10] in the year 1969. The term Semi- closure of a set in a topological space defined and introduced by two mathematician Crossley S. G. & Hildebrand S. K. [3,4] in the year 1971. The mathematician N. Levine [1] also defined and studied the term generalized closed sets in the topological space in Jan 1970. The term Semi- Interior point & Semi-Limit point of a subset of a topological space was defined and studied by the mathematician P. -
Statistics and Probability Letters a Note on Vague Convergence Of
Statistics and Probability Letters 153 (2019) 180–186 Contents lists available at ScienceDirect Statistics and Probability Letters journal homepage: www.elsevier.com/locate/stapro A note on vague convergence of measures ∗ Bojan Basrak, Hrvoje Planini¢ Department of Mathematics, Faculty of Science, University of Zagreb, Bijeni£ka 30, Zagreb, Croatia article info a b s t r a c t Article history: We propose a new approach to vague convergence of measures based on the general Received 17 April 2019 theory of boundedness due to Hu (1966). The article explains how this connects and Accepted 6 June 2019 unifies several frequently used types of vague convergence from the literature. Such Available online 20 June 2019 an approach allows one to translate already developed results from one type of vague MSC: convergence to another. We further analyze the corresponding notion of vague topology primary 28A33 and give a new and useful characterization of convergence in distribution of random secondary 60G57 measures in this topology. 60G70 ' 2019 Elsevier B.V. All rights reserved. Keywords: Boundedly finite measures Vague convergence w#–convergence Random measures Convergence in distribution Lipschitz continuous functions 1. Introduction Let X be a Polish space, i.e. separable topological space which is metrizable by a complete metric. Denote by B(X) the corresponding Borel σ –field and choose a subfamily Bb(X) ⊆ B(X) of sets, called bounded (Borel) sets of X. When there is no fear of confusion, we will simply write B and Bb. A Borel measure µ on X is said to be locally (or boundedly) finite if µ(B) < 1 for all B 2 Bb. -
Weak Convergence of Measures
Mathematical Surveys and Monographs Volume 234 Weak Convergence of Measures Vladimir I. Bogachev Weak Convergence of Measures Mathematical Surveys and Monographs Volume 234 Weak Convergence of Measures Vladimir I. Bogachev EDITORIAL COMMITTEE Walter Craig Natasa Sesum Robert Guralnick, Chair Benjamin Sudakov Constantin Teleman 2010 Mathematics Subject Classification. Primary 60B10, 28C15, 46G12, 60B05, 60B11, 60B12, 60B15, 60E05, 60F05, 54A20. For additional information and updates on this book, visit www.ams.org/bookpages/surv-234 Library of Congress Cataloging-in-Publication Data Names: Bogachev, V. I. (Vladimir Igorevich), 1961- author. Title: Weak convergence of measures / Vladimir I. Bogachev. Description: Providence, Rhode Island : American Mathematical Society, [2018] | Series: Mathe- matical surveys and monographs ; volume 234 | Includes bibliographical references and index. Identifiers: LCCN 2018024621 | ISBN 9781470447380 (alk. paper) Subjects: LCSH: Probabilities. | Measure theory. | Convergence. Classification: LCC QA273.43 .B64 2018 | DDC 519.2/3–dc23 LC record available at https://lccn.loc.gov/2018024621 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy select pages for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given. Republication, systematic copying, or multiple reproduction of any material in this publication is permitted only under license from the American Mathematical Society. Requests for permission to reuse portions of AMS publication content are handled by the Copyright Clearance Center. For more information, please visit www.ams.org/publications/pubpermissions. Send requests for translation rights and licensed reprints to [email protected]. -
Nonlinear Structures Determined by Measures on Banach Spaces Mémoires De La S
MÉMOIRES DE LA S. M. F. K. DAVID ELWORTHY Nonlinear structures determined by measures on Banach spaces Mémoires de la S. M. F., tome 46 (1976), p. 121-130 <http://www.numdam.org/item?id=MSMF_1976__46__121_0> © Mémoires de la S. M. F., 1976, tous droits réservés. L’accès aux archives de la revue « Mémoires de la S. M. F. » (http://smf. emath.fr/Publications/Memoires/Presentation.html) implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright. Article numérisé dans le cadre du programme Numérisation de documents anciens mathématiques http://www.numdam.org/ Journees Geom. dimens. infinie [1975 - LYON ] 121 Bull. Soc. math. France, Memoire 46, 1976, p. 121 - 130. NONLINEAR STRUCTURES DETERMINED BY MEASURES ON BANACH SPACES By K. David ELWORTHY 0. INTRODUCTION. A. A Gaussian measure y on a separable Banach space E, together with the topolcT- gical vector space structure of E, determines a continuous linear injection i : H -> E, of a Hilbert space H, such that y is induced by the canonical cylinder set measure of H. Although the image of H has measure zero, nevertheless H plays a dominant role in both linear and nonlinear analysis involving y, [ 8] , [9], [10] . The most direct approach to obtaining measures on a Banach manifold M, related to its differential structure, requires a lot of extra structure on the manifold : for example a linear map i : H -> T M for each x in M, and even a subset M-^ of M which has the structure of a Hilbert manifold, [6] , [7]. -
POINT SET TOPOLOGY Definition 1 a Topological Structure On
POINT SET TOPOLOGY De¯nition 1 A topological structure on a set X is a family (X) called open sets and satisfying O ½ P (O ) is closed for arbitrary unions 1 O (O ) is closed for ¯nite intersections. 2 O De¯nition 2 A set with a topological structure is a topological space (X; ) O ; = 2;Ei = x : x Eifor some i = [ [i f 2 2 ;g ; so is always open by (O ) ; 1 ; = 2;Ei = x : x Eifor all i = X \ \i f 2 2 ;g so X is always open by (O2). Examples (i) = (X) the discrete topology. O P (ii) ; X the indiscrete of trivial topology. Of; g These coincide when X has one point. (iii) =the rational line. Q =set of unions of open rational intervals O De¯nition 3 Topological spaces X and X 0 are homomorphic if there is an isomorphism of their topological structures i.e. if there is a bijection (1-1 onto map) of X and X 0 which generates a bijection of and . O O e.g. If X and X are discrete spaces a bijection is a homomorphism. (see also Kelley p102 H). De¯nition 4 A base for a topological structure is a family such that B ½ O every o can be expressed as a union of sets of 2 O B Examples (i) for the discrete topological structure x x2X is a base. f g (ii) for the indiscrete topological structure ; X is a base. f; g (iii) For , topologised as before, the set of bounded open intervals is a base.Q 1 (iv) Let X = 0; 1; 2 f g Let = (0; 1); (1; 2); (0; 12) . -
Lecture 20: Weak Convergence (PDF)
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 20 11/25/2013 Introduction to the theory of weak convergence Content. 1. σ-fields on metric spaces. 2. Kolmogorov σ-field on C[0;T ]. 3. Weak convergence. In the first two sections we review some concepts from measure theory on metric spaces. Then in the last section we begin the discussion of the theory of weak convergence, by stating and proving important Portmentau Theorem, which gives four equivalent definitions of weak convergence. 1 Borel σ-fields on metric space We consider a metric space (S; ρ). To discuss probability measures on metric spaces we first need to introduce σ-fields. Definition 1. Borel σ-field B on S is the field generated by the set of all open sets U ⊂ S. Lemma 1. Suppose S is Polish. Then every open set U ⊂ S can be represented as a countable union of balls B(x; r). Proof. Since S is Polish, we can identify a countable set x1; : : : ; xn;::: which is dense. For each xi 2 U, since U is open we can find a radius ri such that ∗ B(xi; ri) ⊂ U. Fix a constant M > 0. Consider ri = min(arg supfr : B(x; r) ⊂ Ug;M) > 0 and set r = r∗=2. Then [ B(x ; r ) ⊂ U. In i i i:xi2U i i order to finish the proof we need to show that equality [i:xi2U B(xi; ri) = U holds. Consider any x 2 U. There exists 0 < r ≤ M such that B(x; r) ⊂ U. -
Probability Measures on Metric Spaces
Probability measures on metric spaces Onno van Gaans These are some loose notes supporting the first sessions of the seminar Stochastic Evolution Equations organized by Dr. Jan van Neerven at the Delft University of Technology during Winter 2002/2003. They contain less information than the common textbooks on the topic of the title. Their purpose is to present a brief selection of the theory that provides a basis for later study of stochastic evolution equations in Banach spaces. The notes aim at an audience that feels more at ease in analysis than in probability theory. The main focus is on Prokhorov's theorem, which serves both as an important tool for future use and as an illustration of techniques that play a role in the theory. The field of measures on topological spaces has the luxury of several excellent textbooks. The main source that has been used to prepare these notes is the book by Parthasarathy [6]. A clear exposition is also available in one of Bour- baki's volumes [2] and in [9, Section 3.2]. The theory on the Prokhorov metric is taken from Billingsley [1]. The additional references for standard facts on general measure theory and general topology have been Halmos [4] and Kelley [5]. Contents 1 Borel sets 2 2 Borel probability measures 3 3 Weak convergence of measures 6 4 The Prokhorov metric 9 5 Prokhorov's theorem 13 6 Riesz representation theorem 18 7 Riesz representation for non-compact spaces 21 8 Integrable functions on metric spaces 24 9 More properties of the space of probability measures 26 1 The distribution of a random variable in a Banach space X will be a probability measure on X.