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1.5. Set Functions and Properties. Let Л Be Any Set of Subsets of E
1.5. Set functions and properties. Let A be any set of subsets of E containing the empty set ∅. A set function is a function µ : A → [0, ∞] with µ(∅)=0. Let µ be a set function. Say that µ is increasing if, for all A, B ∈ A with A ⊆ B, µ(A) ≤ µ(B). Say that µ is additive if, for all disjoint sets A, B ∈ A with A ∪ B ∈ A, µ(A ∪ B)= µ(A)+ µ(B). Say that µ is countably additive if, for all sequences of disjoint sets (An : n ∈ N) in A with n An ∈ A, S µ An = µ(An). n ! n [ X Say that µ is countably subadditive if, for all sequences (An : n ∈ N) in A with n An ∈ A, S µ An ≤ µ(An). n ! n [ X 1.6. Construction of measures. Let A be a set of subsets of E. Say that A is a ring on E if ∅ ∈ A and, for all A, B ∈ A, B \ A ∈ A, A ∪ B ∈ A. Say that A is an algebra on E if ∅ ∈ A and, for all A, B ∈ A, Ac ∈ A, A ∪ B ∈ A. Theorem 1.6.1 (Carath´eodory’s extension theorem). Let A be a ring of subsets of E and let µ : A → [0, ∞] be a countably additive set function. Then µ extends to a measure on the σ-algebra generated by A. Proof. For any B ⊆ E, define the outer measure ∗ µ (B) = inf µ(An) n X where the infimum is taken over all sequences (An : n ∈ N) in A such that B ⊆ n An and is taken to be ∞ if there is no such sequence. -
A Bayesian Level Set Method for Geometric Inverse Problems
Interfaces and Free Boundaries 18 (2016), 181–217 DOI 10.4171/IFB/362 A Bayesian level set method for geometric inverse problems MARCO A. IGLESIAS School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK E-mail: [email protected] YULONG LU Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK E-mail: [email protected] ANDREW M. STUART Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK E-mail: [email protected] [Received 3 April 2015 and in revised form 10 February 2016] We introduce a level set based approach to Bayesian geometric inverse problems. In these problems the interface between different domains is the key unknown, and is realized as the level set of a function. This function itself becomes the object of the inference. Whilst the level set methodology has been widely used for the solution of geometric inverse problems, the Bayesian formulation that we develop here contains two significant advances: firstly it leads to a well-posed inverse problem in which the posterior distribution is Lipschitz with respect to the observed data, and may be used to not only estimate interface locations, but quantify uncertainty in them; and secondly it leads to computationally expedient algorithms in which the level set itself is updated implicitly via the MCMC methodology applied to the level set function – no explicit velocity field is required for the level set interface. Applications are numerous and include medical imaging, modelling of subsurface formations and the inverse source problem; our theory is illustrated with computational results involving the last two applications. -
Generalized Stochastic Processes with Independent Values
GENERALIZED STOCHASTIC PROCESSES WITH INDEPENDENT VALUES K. URBANIK UNIVERSITY OF WROCFAW AND MATHEMATICAL INSTITUTE POLISH ACADEMY OF SCIENCES Let O denote the space of all infinitely differentiable real-valued functions defined on the real line and vanishing outside a compact set. The support of a function so E X, that is, the closure of the set {x: s(x) F 0} will be denoted by s(,o). We shall consider 3D as a topological space with the topology introduced by Schwartz (section 1, chapter 3 in [9]). Any continuous linear functional on this space is a Schwartz distribution, a generalized function. Let us consider the space O of all real-valued random variables defined on the same sample space. The distance between two random variables X and Y will be the Frechet distance p(X, Y) = E[X - Yl/(l + IX - YI), that is, the distance induced by the convergence in probability. Any C-valued continuous linear functional defined on a is called a generalized stochastic process. Every ordinary stochastic process X(t) for which almost all realizations are locally integrable may be considered as a generalized stochastic process. In fact, we make the generalized process T((p) = f X(t),p(t) dt, with sp E 1, correspond to the process X(t). Further, every generalized stochastic process is differentiable. The derivative is given by the formula T'(w) = - T(p'). The theory of generalized stochastic processes was developed by Gelfand [4] and It6 [6]. The method of representation of generalized stochastic processes by means of sequences of ordinary processes was considered in [10]. -
Morita Equivalence and Continuous-Trace C*-Algebras, 1998 59 Paul Howard and Jean E
http://dx.doi.org/10.1090/surv/060 Selected Titles in This Series 60 Iain Raeburn and Dana P. Williams, Morita equivalence and continuous-trace C*-algebras, 1998 59 Paul Howard and Jean E. Rubin, Consequences of the axiom of choice, 1998 58 Pavel I. Etingof, Igor B. Frenkel, and Alexander A. Kirillov, Jr., Lectures on representation theory and Knizhnik-Zamolodchikov equations, 1998 57 Marc Levine, Mixed motives, 1998 56 Leonid I. Korogodski and Yan S. Soibelman, Algebras of functions on quantum groups: Part I, 1998 55 J. Scott Carter and Masahico Saito, Knotted surfaces and their diagrams, 1998 54 Casper Goffman, Togo Nishiura, and Daniel Waterman, Homeomorphisms in analysis, 1997 53 Andreas Kriegl and Peter W. Michor, The convenient setting of global analysis, 1997 52 V. A. Kozlov, V. G. Maz'ya> and J. Rossmann, Elliptic boundary value problems in domains with point singularities, 1997 51 Jan Maly and William P. Ziemer, Fine regularity of solutions of elliptic partial differential equations, 1997 50 Jon Aaronson, An introduction to infinite ergodic theory, 1997 49 R. E. Showalter, Monotone operators in Banach space and nonlinear partial differential equations, 1997 48 Paul-Jean Cahen and Jean-Luc Chabert, Integer-valued polynomials, 1997 47 A. D. Elmendorf, I. Kriz, M. A. Mandell, and J. P. May (with an appendix by M. Cole), Rings, modules, and algebras in stable homotopy theory, 1997 46 Stephen Lipscomb, Symmetric inverse semigroups, 1996 45 George M. Bergman and Adam O. Hausknecht, Cogroups and co-rings in categories of associative rings, 1996 44 J. Amoros, M. Burger, K. Corlette, D. -
Old Notes from Warwick, Part 1
Measure Theory, MA 359 Handout 1 Valeriy Slastikov Autumn, 2005 1 Measure theory 1.1 General construction of Lebesgue measure In this section we will do the general construction of σ-additive complete measure by extending initial σ-additive measure on a semi-ring to a measure on σ-algebra generated by this semi-ring and then completing this measure by adding to the σ-algebra all the null sets. This section provides you with the essentials of the construction and make some parallels with the construction on the plane. Throughout these section we will deal with some collection of sets whose elements are subsets of some fixed abstract set X. It is not necessary to assume any topology on X but for simplicity you may imagine X = Rn. We start with some important definitions: Definition 1.1 A nonempty collection of sets S is a semi-ring if 1. Empty set ? 2 S; 2. If A 2 S; B 2 S then A \ B 2 S; n 3. If A 2 S; A ⊃ A1 2 S then A = [k=1Ak, where Ak 2 S for all 1 ≤ k ≤ n and Ak are disjoint sets. If the set X 2 S then S is called semi-algebra, the set X is called a unit of the collection of sets S. Example 1.1 The collection S of intervals [a; b) for all a; b 2 R form a semi-ring since 1. empty set ? = [a; a) 2 S; 2. if A 2 S and B 2 S then A = [a; b) and B = [c; d). -
Set Notation and Concepts
Appendix Set Notation and Concepts “In mathematics you don’t understand things. You just get used to them.” John von Neumann (1903–1957) This appendix is primarily a brief run-through of basic concepts from set theory, but it also in Sect. A.4 mentions set equations, which are not always covered when introducing set theory. A.1 Basic Concepts and Notation A set is a collection of items. You can write a set by listing its elements (the items it contains) inside curly braces. For example, the set that contains the numbers 1, 2 and 3 can be written as {1, 2, 3}. The order of elements do not matter in a set, so the same set can be written as {2, 1, 3}, {2, 3, 1} or using any permutation of the elements. The number of occurrences also does not matter, so we could also write the set as {2, 1, 2, 3, 1, 1} or in an infinity of other ways. All of these describe the same set. We will normally write sets without repetition, but the fact that repetitions do not matter is important to understand the operations on sets. We will typically use uppercase letters to denote sets and lowercase letters to denote elements in a set, so we could write M ={2, 1, 3} and x = 2 as an element of M. The empty set can be written either as an empty list of elements ({})orusing the special symbol ∅. The latter is more common in mathematical texts. A.1.1 Operations and Predicates We will often need to check if an element belongs to a set or select an element from a set. -
6.436J / 15.085J Fundamentals of Probability, Lecture 4: Random
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2018 Lecture 4 RANDOM VARIABLES Contents 1. Random variables and measurable functions 2. Cumulative distribution functions 3. Discrete random variables 4. Continuous random variables A. Continuity B. The Cantor set, an unusual random variable, and a singular measure 1 RANDOM VARIABLES AND MEASURABLE FUNCTIONS Loosely speaking a random variable provides us with a numerical value, depend- ing on the outcome of an experiment. More precisely, a random variable can be viewed as a function from the sample space to the real numbers, and we will use the notation X(!) to denote the numerical value of a random variable X, when the outcome of the experiment is some particular !. We may be interested in the probability that the outcome of the experiment is such that X is no larger than some c, i.e., that the outcome belongs to the set ! X(!) c . Of course, in { | ≤ } order to have a probability assigned to that set, we need to make sure that it is -measurable. This motivates Definition 1 below. F Example 1. Consider a sequence of five consecutive coin tosses. An appropriate sample space is Ω = 0, 1 n , where “1” stands for heads and “0” for tails. Let be the col- { } F lection of all subsets of Ω , and suppose that a probability measure P has been assigned to ( Ω, ). We are interested in the number of heads obtained in this experiment. This F quantity can be described by the function X : Ω R, defined by → X(!1,...,! ) = !1 + + ! . n ··· n 1 With this definition, the set ! X(!) < 4 is just the event that there were fewer than { | } 4 heads overall, belongs to the ˙-field , and therefore has a well-defined probability. -
Set-Valued Measures
transactions of the american mathematical society Volume 165, March 1972 SET-VALUED MEASURES BY ZVI ARTSTEIN Abstract. A set-valued measure is a u-additive set-function which takes on values in the nonempty subsets of a euclidean space. It is shown that a bounded and non- atomic set-valued measure has convex values. Also the existence of selectors (vector- valued measures) is investigated. The Radon-Nikodym derivative of a set-valued measure is a set-valued function. A general theorem on the existence of R.-N. derivatives is established. The techniques require investigations of measurable set- valued functions and their support functions. 1. Introduction. Let (F, 3~) be a measurable space, and S be a finite-dimensional real vector space. A set-valued measure O on (F, 3~) is a function from T to the nonempty subsets of S, which is countably additive, i.e. 0(Uî°=i £/)Œ2f-i ®(Ei) for every sequence Eu F2,..., of mutually disjoint elements of 3~. Here the sum 2™=i Aj of the subsets Au A2,..., of S, consists of all the vectors a = 2y°=i ßy> where the series is absolutely convergent, and a, e A¡ for every7= 1,2,.... The calculus of set-valued functions has recently been developed by several authors. (See Aumann [1], Banks and Jacobs [3], Bridgland [4], Debreu [7], Hermes [11], Hukuhara [15], and Jacobs [16].) The ideas and techniques have many interesting applications in mathematical economics, [2], [7], [13]; in control theory, [11], [12], [16]; and in other mathematical fields (see [3] and [4] for exten- sive lists of references). -
A Correspondence Between Maximal Abelian Sub-Algebras and Linear
Under consideration for publication in Math. Struct. in Comp. Science A Correspondence between Maximal Abelian Sub-Algebras and Linear Logic Fragments THOMAS SEILLER† 1 † I.H.E.S., Le Bois-Marie, 35, Route de Chartres, 91440 Bures-sur-Yvette, France [email protected] Received December 15th, 2014; Revised September 23rd, 2015 We show a correspondence between a classification of maximal abelian sub-algebras (MASAs) proposed by Jacques Dixmier (Dix54) and fragments of linear logic. We expose for this purpose a modified construction of Girard’s hyperfinite geometry of interaction (Gir11). The expressivity of the logic soundly interpreted in this model is dependent on properties of a MASA which is a parameter of the interpretation. We also unveil the essential role played by MASAs in previous geometry of interaction constructions. Contents 1 Introduction 1 2 von Neumann Algebras and MASAs 4 3 Geometry of Interaction 14 4 Subjective Truth and Matricial GoI 29 5 Dixmier’s Classification and Linear Logic 46 6 Conclusion 58 References 59 arXiv:1408.2125v2 [math.LO] 23 Sep 2015 1. Introduction 1.1. Geometry of Interaction. Geometry of Interaction is a research program initiated by Girard (Gir89b) a year after his seminal paper on linear logic (Gir87a). Its aim is twofold: define a semantics of proofs that accounts for the dynamics of cut-elimination, and then construct realisability models for linear logic around this semantics of cut-elimination. The first step for defining a GoI model, i.e. a construction that fulfills the geometry of 1 This work was partly supported by the ANR-10-BLAN-0213 Logoi. -
Armand Borel 1923–2003
Armand Borel 1923–2003 A Biographical Memoir by Mark Goresky ©2019 National Academy of Sciences. Any opinions expressed in this memoir are those of the author and do not necessarily reflect the views of the National Academy of Sciences. ARMAND BOREL May 21, 1923–August 11, 2003 Elected to the NAS, 1987 Armand Borel was a leading mathematician of the twen- tieth century. A native of Switzerland, he spent most of his professional life in Princeton, New Jersey, where he passed away after a short illness in 2003. Although he is primarily known as one of the chief archi- tects of the modern theory of linear algebraic groups and of arithmetic groups, Borel had an extraordinarily wide range of mathematical interests and influence. Perhaps more than any other mathematician in modern times, Borel elucidated and disseminated the work of others. family the Borel of Photo courtesy His books, conference proceedings, and journal publica- tions provide the document of record for many important mathematical developments during his lifetime. By Mark Goresky Mathematical objects and results bearing Borel’s name include Borel subgroups, Borel regulator, Borel construction, Borel equivariant cohomology, Borel-Serre compactification, Bailey- Borel compactification, Borel fixed point theorem, Borel-Moore homology, Borel-Weil theorem, Borel-de Siebenthal theorem, and Borel conjecture.1 Borel was awarded the Brouwer Medal (Dutch Mathematical Society), the Steele Prize (American Mathematical Society) and the Balzan Prize (Italian-Swiss International Balzan Foundation). He was a member of the National Academy of Sciences (USA), the American Academy of Arts and Sciences, the American Philosophical Society, the Finnish Academy of Sciences and Letters, the Academia Europa, and the French Academy of Sciences.1 1 Borel enjoyed pointing out, with some amusement, that he was not related to the famous Borel of “Borel sets." 2 ARMAND BOREL Switzerland and France Armand Borel was born in 1923 in the French-speaking city of La Chaux-de-Fonds in Switzerland. -
Transcription of an Interview of Jacques Dixmier by Martin Raussen
Transcription of an interview of Jacques Dixmier by Martin Raussen (Aalborg, Denmark) 1 Education Can we start with your early years ? You were born in 1924. Where did you grow up and what was school education like in the years between the two world wars ? I was born in Saint-Étienne, not far from Lyon. Since my parents were tea- chers, both of them, we travelled around France, so I am not from a particular place. I lived in Rouen then Lille and then in Versailles, from when I was nine years old. That was the most important place for me. Your school education was mostly in Versailles ? Yes, in Versailles from 6th degree to mathématiques spéciales (2 years after the baccalauréat 1) except, due to the war, one year in St.-Brieuc, which is a small town in Bretagne. Even there I had a very good teacher in mathematics. And that was probably important ? That was extremely important. I had very good teachers in mathematics for my whole period in school. Only during the 6th grade, the teacher was absolutely zero. But after that, during almost 10 years, each year I had a wonderful professor of mathematics. That is probably one of the main rea- sons for my vocation. Can you remember a particular instance that made you think : mathematics, that is something for me ? I always liked mathematics but originally I did not believe that I was able to do research. I realised that I liked teaching and I thought I would be a good professor of mathematics. -
Quaderni Del Trentennale 1975-2005
ISTITUTO ITALIANO PER GLI STUDI FILOSOFICI QUADERNI DEL TRENTENNALE 1975-2005 3 1 2 ISTITUTO ITALIANO PER GLI STUDI FILOSOFICI LEZIONI DI PREMI NOBEL Nella sede dell’Istituto Napoli 2005 3 A cura di Antonio Gargano, Segretario generale dell’Istituto Italiano per gli Studi Filosofici @ Istituto Italiano per gli Studi Filosofici Palazzo Serra di Cassano Napoli, Via Monte di Dio 14 4 INDICE PREMESSA 7 EDUARDO CAIANIELLO, Napoli perla della scienza 23 AUGUSTO GRAZIANI, Introduction to the essays of Kenneth J. Arrow 29 KENNETH J. ARROW, The Economics of Information 35 KENNETH J. ARROW, General Equilibrium and Economic Growth 47 SHELDON L. GLASHOW, La sfida della fisica delle particelle elementari 55 FRANCESCO NICODEMI, Premessa al testo di David Gross 57 DAVID GROSS, Unified Theories of Everything 63 DAVID GROSS, Teorie unificate del tutto 83 MAX F. P ERUTZ, Emoglobina, una molecola vivente 101 ILYA PRIGOGINE, Vers un humanisme scientifique 105 RITA LEVI MONTALCINI, Un nuovo ordine internazionale per la sicurezza dell’umanità 119 AUGUSTO GRAZIANI, Premessa al testo di James Tobin 123 JAMES TOBIN, Price Flexibility and Full Employment. The Debate Then and Now 133 5 SEMINARI E GIORNATE DI STUDIO DI SCIENZE E STORIA DELLE SCIENZE 147 SEMINARI E GIORNATE DI STUDIO DI STORIA E TEORIA ECONOMICA 331 6 PREMESSA L’Istituto Italiano per gli Studi Filosofici fin dalla sua fondazione ha costantemente affiancato alle attività di formazione e di ricerca in ambito filosofico e storico iniziative di ampio respiro nel campo delle scienze matematiche e naturali, persuaso della fondamentale unità della conoscenza nella ricerca della verità. Proprio al tema Unity and Internationalism of the Sciences and Humanities l’Istituto dedicò un convegno nella sede del CERN a Ginevra, alla presenza di Edoardo Amaldi.