A Convenient Category for Higher-Order Probability Theory
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Jointly Measurable and Progressively Measurable Stochastic Processes
Jointly measurable and progressively measurable stochastic processes Jordan Bell [email protected] Department of Mathematics, University of Toronto June 18, 2015 1 Jointly measurable stochastic processes d Let E = R with Borel E , let I = R≥0, which is a topological space with the subspace topology inherited from R, and let (Ω; F ;P ) be a probability space. For a stochastic process (Xt)t2I with state space E, we say that X is jointly measurable if the map (t; !) 7! Xt(!) is measurable BI ⊗ F ! E . For ! 2 Ω, the path t 7! Xt(!) is called left-continuous if for each t 2 I, Xs(!) ! Xt(!); s " t: We prove that if the paths of a stochastic process are left-continuous then the stochastic process is jointly measurable.1 Theorem 1. If X is a stochastic process with state space E and all the paths of X are left-continuous, then X is jointly measurable. Proof. For n ≥ 1, t 2 I, and ! 2 Ω, let 1 n X Xt (!) = 1[k2−n;(k+1)2−n)(t)Xk2−n (!): k=0 n Each X is measurable BI ⊗ F ! E : for B 2 E , 1 n [ −n −n f(t; !) 2 I × Ω: Xt (!) 2 Bg = [k2 ; (k + 1)2 ) × fXk2−n 2 Bg: k=0 −n −n Let t 2 I. For each n there is a unique kn for which t 2 [kn2 ; (kn +1)2 ), and n −n −n thus Xt (!) = Xkn2 (!). Furthermore, kn2 " t, and because s 7! Xs(!) is n −n left-continuous, Xkn2 (!) ! Xt(!). -
Shape Analysis, Lebesgue Integration and Absolute Continuity Connections
NISTIR 8217 Shape Analysis, Lebesgue Integration and Absolute Continuity Connections Javier Bernal This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8217 NISTIR 8217 Shape Analysis, Lebesgue Integration and Absolute Continuity Connections Javier Bernal Applied and Computational Mathematics Division Information Technology Laboratory This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8217 July 2018 INCLUDES UPDATES AS OF 07-18-2018; SEE APPENDIX U.S. Department of Commerce Wilbur L. Ross, Jr., Secretary National Institute of Standards and Technology Walter Copan, NIST Director and Undersecretary of Commerce for Standards and Technology ______________________________________________________________________________________________________ This Shape Analysis, Lebesgue Integration and publication Absolute Continuity Connections Javier Bernal is National Institute of Standards and Technology, available Gaithersburg, MD 20899, USA free of Abstract charge As shape analysis of the form presented in Srivastava and Klassen’s textbook “Functional and Shape Data Analysis” is intricately related to Lebesgue integration and absolute continuity, it is advantageous from: to have a good grasp of the latter two notions. Accordingly, in these notes we review basic concepts and results about Lebesgue integration https://doi.org/10.6028/NIST.IR.8217 and absolute continuity. In particular, we review fundamental results connecting them to each other and to the kind of shape analysis, or more generally, functional data analysis presented in the aforeme- tioned textbook, in the process shedding light on important aspects of all three notions. Many well-known results, especially most results about Lebesgue integration and some results about absolute conti- nuity, are presented without proofs. -
LEBESGUE MEASURE and L2 SPACE. Contents 1. Measure Spaces 1 2. Lebesgue Integration 2 3. L2 Space 4 Acknowledgments 9 References
LEBESGUE MEASURE AND L2 SPACE. ANNIE WANG Abstract. This paper begins with an introduction to measure spaces and the Lebesgue theory of measure and integration. Several important theorems regarding the Lebesgue integral are then developed. Finally, we prove the completeness of the L2(µ) space and show that it is a metric space, and a Hilbert space. Contents 1. Measure Spaces 1 2. Lebesgue Integration 2 3. L2 Space 4 Acknowledgments 9 References 9 1. Measure Spaces Definition 1.1. Suppose X is a set. Then X is said to be a measure space if there exists a σ-ring M (that is, M is a nonempty family of subsets of X closed under countable unions and under complements)of subsets of X and a non-negative countably additive set function µ (called a measure) defined on M . If X 2 M, then X is said to be a measurable space. For example, let X = Rp, M the collection of Lebesgue-measurable subsets of Rp, and µ the Lebesgue measure. Another measure space can be found by taking X to be the set of all positive integers, M the collection of all subsets of X, and µ(E) the number of elements of E. We will be interested only in a special case of the measure, the Lebesgue measure. The Lebesgue measure allows us to extend the notions of length and volume to more complicated sets. Definition 1.2. Let Rp be a p-dimensional Euclidean space . We denote an interval p of R by the set of points x = (x1; :::; xp) such that (1.3) ai ≤ xi ≤ bi (i = 1; : : : ; p) Definition 1.4. -
1 Measurable Functions
36-752 Advanced Probability Overview Spring 2018 2. Measurable Functions, Random Variables, and Integration Instructor: Alessandro Rinaldo Associated reading: Sec 1.5 of Ash and Dol´eans-Dade; Sec 1.3 and 1.4 of Durrett. 1 Measurable Functions 1.1 Measurable functions Measurable functions are functions that we can integrate with respect to measures in much the same way that continuous functions can be integrated \dx". Recall that the Riemann integral of a continuous function f over a bounded interval is defined as a limit of sums of lengths of subintervals times values of f on the subintervals. The measure of a set generalizes the length while elements of the σ-field generalize the intervals. Recall that a real-valued function is continuous if and only if the inverse image of every open set is open. This generalizes to the inverse image of every measurable set being measurable. Definition 1 (Measurable Functions). Let (Ω; F) and (S; A) be measurable spaces. Let f :Ω ! S be a function that satisfies f −1(A) 2 F for each A 2 A. Then we say that f is F=A-measurable. If the σ-field’s are to be understood from context, we simply say that f is measurable. Example 2. Let F = 2Ω. Then every function from Ω to a set S is measurable no matter what A is. Example 3. Let A = f?;Sg. Then every function from a set Ω to S is measurable, no matter what F is. Proving that a function is measurable is facilitated by noticing that inverse image commutes with union, complement, and intersection. -
Notes 2 : Measure-Theoretic Foundations II
Notes 2 : Measure-theoretic foundations II Math 733-734: Theory of Probability Lecturer: Sebastien Roch References: [Wil91, Chapters 4-6, 8], [Dur10, Sections 1.4-1.7, 2.1]. 1 Independence 1.1 Definition of independence Let (Ω; F; P) be a probability space. DEF 2.1 (Independence) Sub-σ-algebras G1; G2;::: of F are independent for all Gi 2 Gi, i ≥ 1, and distinct i1; : : : ; in we have n Y P[Gi1 \···\ Gin ] = P[Gij ]: j=1 Specializing to events and random variables: DEF 2.2 (Independent RVs) RVs X1;X2;::: are independent if the σ-algebras σ(X1); σ(X2);::: are independent. DEF 2.3 (Independent Events) Events E1;E2;::: are independent if the σ-algebras c Ei = f;;Ei;Ei ; Ωg; i ≥ 1; are independent. The more familiar definitions are the following: THM 2.4 (Independent RVs: Familiar definition) RVs X, Y are independent if and only if for all x; y 2 R P[X ≤ x; Y ≤ y] = P[X ≤ x]P[Y ≤ y]: THM 2.5 (Independent events: Familiar definition) Events E1, E2 are indepen- dent if and only if P[E1 \ E2] = P[E1]P[E2]: 1 Lecture 2: Measure-theoretic foundations II 2 The proofs of these characterizations follows immediately from the following lemma. LEM 2.6 (Independence and π-systems) Suppose that G and H are sub-σ-algebras and that I and J are π-systems such that σ(I) = G; σ(J ) = H: Then G and H are independent if and only if I and J are, i.e., P[I \ J] = P[I]P[J]; 8I 2 I;J 2 J : Proof: Suppose I and J are independent. -
Basic Category Theory and Topos Theory
Basic Category Theory and Topos Theory Jaap van Oosten Jaap van Oosten Department of Mathematics Utrecht University The Netherlands Revised, February 2016 Contents 1 Categories and Functors 1 1.1 Definitions and examples . 1 1.2 Some special objects and arrows . 5 2 Natural transformations 8 2.1 The Yoneda lemma . 8 2.2 Examples of natural transformations . 11 2.3 Equivalence of categories; an example . 13 3 (Co)cones and (Co)limits 16 3.1 Limits . 16 3.2 Limits by products and equalizers . 23 3.3 Complete Categories . 24 3.4 Colimits . 25 4 A little piece of categorical logic 28 4.1 Regular categories and subobjects . 28 4.2 The logic of regular categories . 34 4.3 The language L(C) and theory T (C) associated to a regular cat- egory C ................................ 39 4.4 The category C(T ) associated to a theory T : Completeness Theorem 41 4.5 Example of a regular category . 44 5 Adjunctions 47 5.1 Adjoint functors . 47 5.2 Expressing (co)completeness by existence of adjoints; preserva- tion of (co)limits by adjoint functors . 52 6 Monads and Algebras 56 6.1 Algebras for a monad . 57 6.2 T -Algebras at least as complete as D . 61 6.3 The Kleisli category of a monad . 62 7 Cartesian closed categories and the λ-calculus 64 7.1 Cartesian closed categories (ccc's); examples and basic facts . 64 7.2 Typed λ-calculus and cartesian closed categories . 68 7.3 Representation of primitive recursive functions in ccc's with nat- ural numbers object . -
SHORT INTRODUCTION to ENRICHED CATEGORIES FRANCIS BORCEUX and ISAR STUBBE Département De Mathématique, Université Catholique
SHORT INTRODUCTION TO ENRICHED CATEGORIES FRANCIS BORCEUX and ISAR STUBBE Departement de Mathematique, Universite Catholique de Louvain, 2 Ch. du Cyclotron, B-1348 Louvain-la-Neuve, Belgium. e-mail: [email protected] — [email protected] This text aims to be a short introduction to some of the basic notions in ordinary and enriched category theory. With reasonable detail but always in a compact fashion, we have brought together in the rst part of this paper the denitions and basic properties of such notions as limit and colimit constructions in a category, adjoint functors between categories, equivalences and monads. In the second part we pass on to enriched category theory: it is explained how one can “replace” the category of sets and mappings, which plays a crucial role in ordinary category theory, by a more general symmetric monoidal closed category, and how most results of ordinary category theory can be translated to this more general setting. For a lack of space we had to omit detailed proofs, but instead we have included lots of examples which we hope will be helpful. In any case, the interested reader will nd his way to the references, given at the end of the paper. 1. Ordinary categories When working with vector spaces over a eld K, one proves such theorems as: for all vector spaces there exists a base; every vector space V is canonically included in its bidual V ; every linear map between nite dimensional based vector spaces can be represented as a matrix; and so on. But where do the universal quantiers take their value? What precisely does “canonical” mean? How can we formally “compare” vector spaces with matrices? What is so special about vector spaces that they can be based? An answer to these questions, and many more, can be formulated in a very precise way using the language of category theory. -
(Measure Theory for Dummies) UWEE Technical Report Number UWEETR-2006-0008
A Measure Theory Tutorial (Measure Theory for Dummies) Maya R. Gupta {gupta}@ee.washington.edu Dept of EE, University of Washington Seattle WA, 98195-2500 UWEE Technical Report Number UWEETR-2006-0008 May 2006 Department of Electrical Engineering University of Washington Box 352500 Seattle, Washington 98195-2500 PHN: (206) 543-2150 FAX: (206) 543-3842 URL: http://www.ee.washington.edu A Measure Theory Tutorial (Measure Theory for Dummies) Maya R. Gupta {gupta}@ee.washington.edu Dept of EE, University of Washington Seattle WA, 98195-2500 University of Washington, Dept. of EE, UWEETR-2006-0008 May 2006 Abstract This tutorial is an informal introduction to measure theory for people who are interested in reading papers that use measure theory. The tutorial assumes one has had at least a year of college-level calculus, some graduate level exposure to random processes, and familiarity with terms like “closed” and “open.” The focus is on the terms and ideas relevant to applied probability and information theory. There are no proofs and no exercises. Measure theory is a bit like grammar, many people communicate clearly without worrying about all the details, but the details do exist and for good reasons. There are a number of great texts that do measure theory justice. This is not one of them. Rather this is a hack way to get the basic ideas down so you can read through research papers and follow what’s going on. Hopefully, you’ll get curious and excited enough about the details to check out some of the references for a deeper understanding. -
1 Probability Measure and Random Variables
1 Probability measure and random variables 1.1 Probability spaces and measures We will use the term experiment in a very general way to refer to some process that produces a random outcome. Definition 1. The set of possible outcomes is called the sample space. We will typically denote an individual outcome by ω and the sample space by Ω. Set notation: A B, A is a subset of B, means that every element of A is also in B. The union⊂ A B of A and B is the of all elements that are in A or B, including those that∪ are in both. The intersection A B of A and B is the set of all elements that are in both of A and B. ∩ n j=1Aj is the set of elements that are in at least one of the Aj. ∪n j=1Aj is the set of elements that are in all of the Aj. ∩∞ ∞ j=1Aj, j=1Aj are ... Two∩ sets A∪ and B are disjoint if A B = . denotes the empty set, the set with no elements. ∩ ∅ ∅ Complements: The complement of an event A, denoted Ac, is the set of outcomes (in Ω) which are not in A. Note that the book writes it as Ω A. De Morgan’s laws: \ (A B)c = Ac Bc ∪ ∩ (A B)c = Ac Bc ∩ ∪ c c ( Aj) = Aj j j [ \ c c ( Aj) = Aj j j \ [ (1) Definition 2. Let Ω be a sample space. A collection of subsets of Ω is a σ-field if F 1. -
The Ito Integral
Notes on the Itoˆ Calculus Steven P. Lalley May 15, 2012 1 Continuous-Time Processes: Progressive Measurability 1.1 Progressive Measurability Measurability issues are a bit like plumbing – a bit ugly, and most of the time you would prefer that it remains hidden from view, but sometimes it is unavoidable that you actually have to open it up and work on it. In the theory of continuous–time stochastic processes, measurability problems are usually more subtle than in discrete time, primarily because sets of measures 0 can add up to something significant when you put together uncountably many of them. In stochastic integration there is another issue: we must be sure that any stochastic processes Xt(!) that we try to integrate are jointly measurable in (t; !), so as to ensure that the integrals are themselves random variables (that is, measurable in !). Assume that (Ω; F;P ) is a fixed probability space, and that all random variables are F−measurable. A filtration is an increasing family F := fFtgt2J of sub-σ−algebras of F indexed by t 2 J, where J is an interval, usually J = [0; 1). Recall that a stochastic process fYtgt≥0 is said to be adapted to a filtration if for each t ≥ 0 the random variable Yt is Ft−measurable, and that a filtration is admissible for a Wiener process fWtgt≥0 if for each t > 0 the post-t increment process fWt+s − Wtgs≥0 is independent of Ft. Definition 1. A stochastic process fXtgt≥0 is said to be progressively measurable if for every T ≥ 0 it is, when viewed as a function X(t; !) on the product space ([0;T ])×Ω, measurable relative to the product σ−algebra B[0;T ] × FT . -
On Closed Categories of Functors
ON CLOSED CATEGORIES OF FUNCTORS Brian Day Received November 7, 19~9 The purpose of the present paper is to develop in further detail the remarks, concerning the relationship of Kan functor extensions to closed structures on functor categories, made in "Enriched functor categories" | 1] §9. It is assumed that the reader is familiar with the basic results of closed category theory, including the representation theorem. Apart from some minor changes mentioned below, the terminology and notation employed are those of |i], |3], and |5]. Terminology A closed category V in the sense of Eilenberg and Kelly |B| will be called a normalised closed category, V: V o ÷ En6 being the normalisation. Throughout this paper V is taken to be a fixed normalised symmetric monoidal closed category (Vo, @, I, r, £, a, c, V, |-,-|, p) with V ° admitting all small limits (inverse limits) and colimits (direct limits). It is further supposed that if the limit or colimit in ~o of a functor (with possibly large domain) exists then a definite choice has been made of it. In short, we place on V those hypotheses which both allow it to replace the cartesian closed category of (small) sets Ens as a ground category and are satisfied by most "natural" closed categories. As in [i], an end in B of a V-functor T: A°P@A ÷ B is a Y-natural family mA: K ÷ T(AA) of morphisms in B o with the property that the family B(1,mA): B(BK) ÷ B(B,T(AA)) in V o is -2- universally V-natural in A for each B 6 B; then an end in V turns out to be simply a family sA: K ~ T(AA) of morphisms in V o which is universally V-natural in A. -
Lebesgue Differentiation
LEBESGUE DIFFERENTIATION ANUSH TSERUNYAN Throughout, we work in the Lebesgue measure space Rd;L(Rd);λ , where L(Rd) is the σ-algebra of Lebesgue-measurable sets and λ is the Lebesgue measure. 2 Rd For r > 0 and x , let Br(x) denote the open ball of radius r centered at x. The spaces 0 and 1 1. L Lloc The space L0 of measurable functions. By a measurable function we mean an L(Rd)- measurable function f : Rd ! R and we let L0(Rd) (or just L0) denote the vector space of all measurable functions modulo the usual equivalence relation =a:e: of a.e. equality. There are two natural notions of convergence (topologies) on L0: a.e. convergence and convergence in measure. As we know, these two notions are related, but neither implies the other. However, convergence in measure of a sequence implies a.e. convergence of a subsequence, which in many situations can be boosted to a.e. convergence of the entire sequence (recall Problem 3 of Midterm 2). Convergence in measure is captured by the following family of norm-like maps: for each α > 0 and f 2 L0, k k . · f α∗ .= α λ ∆α(f ) ; n o . 2 Rd j j k k where ∆α(f ) .= x : f (x) > α . Note that f α∗ can be infinite. 2 0 k k 6 k k Observation 1 (Chebyshev’s inequality). For any α > 0 and any f L , f α∗ f 1. 1 2 Rd The space Lloc of locally integrable functions. Differentiation at a point x is a local notion as it only depends on the values of the function in some open neighborhood of x, so, in this context, it is natural to work with a class of functions defined via a local property, as opposed to global (e.g.