Theory of Means and Their Inequalities
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Three Problems of the Theory of Choice on Random Sets
DIVISION OF THE HUMANITIES AND SOCIAL SCIENCES CALIFORNIA INSTITUTE OF TECHNOLOGY PASADENA, CALIFORNIA 91125 THREE PROBLEMS OF THE THEORY OF CHOICE ON RANDOM SETS B. A. Berezovskiy,* Yu. M. Baryshnikov, A. Gnedin V. Institute of Control Sciences Academy of Sciences of the U.S.S.R, Moscow *Guest of the California Institute of Technology SOCIAL SCIENCE WORKING PAPER 661 December 1987 THREE PROBLEMS OF THE THEORY OF CHOICE ON RANDOM SETS B. A. Berezovskiy, Yu. M. Baryshnikov, A. V. Gnedin Institute of Control Sciences Academy of Sciences of the U.S.S.R., Moscow ABSTRACT This paper discusses three problems which are united not only by the common topic of research stated in thetitle, but also by a somewhat surprising interlacing of the methods and techniques used. In the first problem, an attempt is made to resolve a very unpleasant metaproblem arising in general choice theory: why theconditions of rationality are not really necessary or, in other words, why in every-day life we are quite satisfied with choice methods which are far from being ideal. The answer, substantiated by a number of results, is as follows: situations in which the choice function "misbehaves" are very seldom met in large presentations. the second problem, an overview of our studies is given on the problem of statistical propertiesIn of choice. One of themost astonishing phenomenon found when we deviate from scalar extremal choice functions is in stable multiplicity of choice. If our presentation is random, then a random number of alternatives is chosen in it. But how many? The answer isn't trival, and may be sought in many differentdirections. -
AMS / MAA CLASSROOM RESOURCE MATERIALS VOL 35 I I “Master” — 2011/5/9 — 10:51 — Page I — #1 I I
AMS / MAA CLASSROOM RESOURCE MATERIALS VOL 35 i i “master” — 2011/5/9 — 10:51 — page i — #1 i i 10.1090/clrm/035 Excursions in Classical Analysis Pathways to Advanced Problem Solving and Undergraduate Research i i i i i i “master” — 2011/5/9 — 10:51 — page ii — #2 i i Chapter 11 (Generating Functions for Powers of Fibonacci Numbers) is a reworked version of material from my same name paper in the International Journalof Mathematical Education in Science and Tech- nology, Vol. 38:4 (2007) pp. 531–537. www.informaworld.com Chapter 12 (Identities for the Fibonacci Powers) is a reworked version of material from my same name paper in the International Journal of Mathematical Education in Science and Technology, Vol. 39:4 (2008) pp. 534–541. www.informaworld.com Chapter 13 (Bernoulli Numbers via Determinants)is a reworked ver- sion of material from my same name paper in the International Jour- nal of Mathematical Education in Science and Technology, Vol. 34:2 (2003) pp. 291–297. www.informaworld.com Chapter 19 (Parametric Differentiation and Integration) is a reworked version of material from my same name paper in the Interna- tional Journal of Mathematical Education in Science and Technology, Vol. 40:4 (2009) pp. 559–570. www.informaworld.com c 2010 by the Mathematical Association of America, Inc. Library of Congress Catalog Card Number 2010924991 Print ISBN 978-0-88385-768-7 Electronic ISBN 978-0-88385-935-3 Printed in the United States of America Current Printing (last digit): 10987654321 i i i i i i “master” — 2011/5/9 — 10:51 — page iii — #3 i i Excursions in Classical Analysis Pathways to Advanced Problem Solving and Undergraduate Research Hongwei Chen Christopher Newport University Published and Distributed by The Mathematical Association of America i i i i i i “master” — 2011/5/9 — 10:51 — page iv — #4 i i Committee on Books Frank Farris, Chair Classroom Resource Materials Editorial Board Gerald M. -
The Halász-Székely Barycenter
THE HALÁSZ–SZÉKELY BARYCENTER JAIRO BOCHI, GODOFREDO IOMMI, AND MARIO PONCE Abstract. We introduce a notion of barycenter of a probability measure re- lated to the symmetric mean of a collection of nonnegative real numbers. Our definition is inspired by the work of Halász and Székely, who in 1976 proved a law of large numbers for symmetric means. We study analytic properties of this Halász–Székely barycenter. We establish fundamental inequalities that relate the symmetric mean of a list of nonnegative real numbers with the barycenter of the measure uniformly supported on these points. As consequence, we go on to establish an ergodic theorem stating that the symmetric means of a se- quence of dynamical observations converges to the Halász–Székely barycenter of the corresponding distribution. 1. Introduction Means have fascinated man for a long time. Ancient Greeks knew the arith- metic, geometric, and harmonic means of two positive numbers (which they may have learned from the Babylonians); they also studied other types of means that can be defined using proportions: see [He, pp. 85–89]. Newton and Maclaurin en- countered the symmetric means (more about them later). Huygens introduced the notion of expected value and Jacob Bernoulli proved the first rigorous version of the law of large numbers: see [Mai, pp. 51, 73]. Gauss and Lagrange exploited the connection between the arithmetico-geometric mean and elliptic functions: see [BB]. Kolmogorov and other authors considered means from an axiomatic point of view and determined when a mean is arithmetic under a change of coordinates (i.e. quasiarithmetic): see [HLP, p. -
TOPIC 0 Introduction
TOPIC 0 Introduction 1 Review Course: Math 535 (http://www.math.wisc.edu/~roch/mmids/) - Mathematical Methods in Data Science (MMiDS) Author: Sebastien Roch (http://www.math.wisc.edu/~roch/), Department of Mathematics, University of Wisconsin-Madison Updated: Sep 21, 2020 Copyright: © 2020 Sebastien Roch We first review a few basic concepts. 1.1 Vectors and norms For a vector ⎡ 푥 ⎤ ⎢ 1 ⎥ 푥 ⎢ 2 ⎥ 푑 퐱 = ⎢ ⎥ ∈ ℝ ⎢ ⋮ ⎥ ⎣ 푥푑 ⎦ the Euclidean norm of 퐱 is defined as ‾‾푑‾‾‾‾ ‖퐱‖ = 푥2 = √퐱‾‾푇‾퐱‾ = ‾⟨퐱‾‾, ‾퐱‾⟩ 2 ∑ 푖 √ ⎷푖=1 푇 where 퐱 denotes the transpose of 퐱 (seen as a single-column matrix) and 푑 ⟨퐮, 퐯⟩ = 푢 푣 ∑ 푖 푖 푖=1 is the inner product (https://en.wikipedia.org/wiki/Inner_product_space) of 퐮 and 퐯. This is also known as the 2- norm. More generally, for 푝 ≥ 1, the 푝-norm (https://en.wikipedia.org/wiki/Lp_space#The_p- norm_in_countably_infinite_dimensions_and_ℓ_p_spaces) of 퐱 is given by 푑 1/푝 ‖퐱‖ = |푥 |푝 . 푝 (∑ 푖 ) 푖=1 Here (https://commons.wikimedia.org/wiki/File:Lp_space_animation.gif#/media/File:Lp_space_animation.gif) is a nice visualization of the unit ball, that is, the set {퐱 : ‖푥‖푝 ≤ 1}, under varying 푝. There exist many more norms. Formally: 푑 푑 Definition (Norm): A norm is a function ℓ from ℝ to ℝ+ that satisfies for all 푎 ∈ ℝ, 퐮, 퐯 ∈ ℝ (Homogeneity): ℓ(푎퐮) = |푎|ℓ(퐮) (Triangle inequality): ℓ(퐮 + 퐯) ≤ ℓ(퐮) + ℓ(퐯) (Point-separating): ℓ(푢) = 0 implies 퐮 = 0. ⊲ The triangle inequality for the 2-norm follows (https://en.wikipedia.org/wiki/Cauchy– Schwarz_inequality#Analysis) from the Cauchy–Schwarz inequality (https://en.wikipedia.org/wiki/Cauchy– Schwarz_inequality). -
Continuous Probability Distributions Continuous Probability Distributions – a Guide for Teachers (Years 11–12)
1 Supporting Australian Mathematics Project 2 3 4 5 6 12 11 7 10 8 9 A guide for teachers – Years 11 and 12 Probability and statistics: Module 21 Continuous probability distributions Continuous probability distributions – A guide for teachers (Years 11–12) Professor Ian Gordon, University of Melbourne Editor: Dr Jane Pitkethly, La Trobe University Illustrations and web design: Catherine Tan, Michael Shaw Full bibliographic details are available from Education Services Australia. Published by Education Services Australia PO Box 177 Carlton South Vic 3053 Australia Tel: (03) 9207 9600 Fax: (03) 9910 9800 Email: [email protected] Website: www.esa.edu.au © 2013 Education Services Australia Ltd, except where indicated otherwise. You may copy, distribute and adapt this material free of charge for non-commercial educational purposes, provided you retain all copyright notices and acknowledgements. This publication is funded by the Australian Government Department of Education, Employment and Workplace Relations. Supporting Australian Mathematics Project Australian Mathematical Sciences Institute Building 161 The University of Melbourne VIC 3010 Email: [email protected] Website: www.amsi.org.au Assumed knowledge ..................................... 4 Motivation ........................................... 4 Content ............................................. 5 Continuous random variables: basic ideas ....................... 5 Cumulative distribution functions ............................ 6 Probability density functions .............................. -
Sharp Inequalities Between Hölder and Stolarsky Means of Two Positive
Aust. J. Math. Anal. Appl. Vol. 18 (2021), No. 1, Art. 8, 42 pp. AJMAA SHARP INEQUALITIES BETWEEN HÖLDER AND STOLARSKY MEANS OF TWO POSITIVE NUMBERS MARGARITA BUSTOS GONZALEZ AND AUREL IULIAN STAN Received 29 September, 2019; accepted 15 December, 2020; published 12 February, 2021. THE UNIVERSITY OF IOWA,DEPARTMENT OF MATHEMATICS, 14 MACLEAN HALL,IOWA CITY,IOWA, USA. [email protected] THE OHIO STATE UNIVERSITY AT MARION,DEPARTMENT OF MATHEMATICS, 1465 MOUNT VERNON AVENUE,MARION,OHIO, USA. [email protected] ABSTRACT. Given any index of the Stolarsky means, we find the greatest and least indexes of the Hölder means, such that for any two positive numbers, the Stolarsky mean with the given index is bounded from below and above by the Hölder means with those indexes, of the two positive numbers. Finally, we present a geometric application of this inequality involving the Fermat-Torricelli point of a triangle. Key words and phrases: Hölder means; Stolarsky means; Monotone functions; Jensen inequality; Hermite–Hadamard in- equality. 2010 Mathematics Subject Classification. Primary 26D15. Secondary 26D20. ISSN (electronic): 1449-5910 © 2021 Austral Internet Publishing. All rights reserved. This research was started during the Sampling Advanced Mathematics for Minority Students (SAMMS) Program, organized by the Depart- ment of Mathematics of The Ohio State University, during the summer of the year 2018. The authors would like to thank the SAMMS Program for kindly supporting this research. 2 M. BUSTOS GONZALEZ AND A. I. STAN 1. INTRODUCTION The Hölder and Stolarsky means of two positive numbers a and b, with a < b, are obtained by taking a probability measure µ, whose support contains the set fa, bg and is contained in the interval [a, b], integrating the function x 7! xp, for some p 2 [−∞, 1], with respect to that probability measure, and then taking the 1=p power of that integral. -
Probability with Engineering Applications ECE 313 Course Notes
Probability with Engineering Applications ECE 313 Course Notes Bruce Hajek Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign January 2017 c 2017 by Bruce Hajek All rights reserved. Permission is hereby given to freely print and circulate copies of these notes so long as the notes are left intact and not reproduced for commercial purposes. Email to [email protected], pointing out errors or hard to understand passages or providing comments, is welcome. Contents 1 Foundations 3 1.1 Embracing uncertainty . .3 1.2 Axioms of probability . .6 1.3 Calculating the size of various sets . 10 1.4 Probability experiments with equally likely outcomes . 13 1.5 Sample spaces with infinite cardinality . 15 1.6 Short Answer Questions . 20 1.7 Problems . 21 2 Discrete-type random variables 25 2.1 Random variables and probability mass functions . 25 2.2 The mean and variance of a random variable . 27 2.3 Conditional probabilities . 32 2.4 Independence and the binomial distribution . 34 2.4.1 Mutually independent events . 34 2.4.2 Independent random variables (of discrete-type) . 36 2.4.3 Bernoulli distribution . 37 2.4.4 Binomial distribution . 38 2.5 Geometric distribution . 41 2.6 Bernoulli process and the negative binomial distribution . 43 2.7 The Poisson distribution{a limit of binomial distributions . 45 2.8 Maximum likelihood parameter estimation . 47 2.9 Markov and Chebychev inequalities and confidence intervals . 50 2.10 The law of total probability, and Bayes formula . 53 2.11 Binary hypothesis testing with discrete-type observations . -
HARMONIC ANALYSIS 1. Maximal Function for a Locally Integrable
HARMONIC ANALYSIS PIOTR HAJLASZ 1. Maximal function 1 n For a locally integrable function f 2 Lloc(R ) the Hardy-Littlewood max- imal function is defined by Z n Mf(x) = sup jf(y)j dy; x 2 R : r>0 B(x;r) The operator M is not linear but it is subadditive. We say that an operator T from a space of measurable functions into a space of measurable functions is subadditive if jT (f1 + f2)(x)j ≤ jT f1(x)j + jT f2(x)j a.e. and jT (kf)(x)j = jkjjT f(x)j for k 2 C. The following integrability result, known also as the maximal theorem, plays a fundamental role in many areas of mathematical analysis. p n Theorem 1.1 (Hardy-Littlewood-Wiener). If f 2 L (R ), 1 ≤ p ≤ 1, then Mf < 1 a.e. Moreover 1 n (a) For f 2 L (R ) 5n Z (1.1) jfx : Mf(x) > tgj ≤ jfj for all t > 0. t Rn p n p n (b) If f 2 L (R ), 1 < p ≤ 1, then Mf 2 L (R ) and p 1=p kMfk ≤ 2 · 5n=p kfk for 1 < p < 1, p p − 1 p kMfk1 ≤ kfk1 : Date: March 28, 2012. 1 2 PIOTR HAJLASZ The estimate (1.1) is called weak type estimate. 1 n 1 n Note that if f 2 L (R ) is a nonzero function, then Mf 62 L (R ). Indeed, R if λ = B(0;R) jfj > 0, then for jxj > R we have Z λ Mf(x) ≥ jfj ≥ n ; B(x;R+jxj) !n(R + jxj) n and the function on the right hand side is not integrable on R . -
Stolarsky Means and Hadamard's Inequality
JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS 220, 99]109Ž. 1998 ARTICLE NO. AY975822 Stolarsky Means and Hadamard's Inequality C. E. M. Pearce Department of Applied Mathematics, The Uni¨ersity of Adelaide, Adelaide, SA 5005, Australia View metadata, citation and similar papers at core.ac.uk brought to you by CORE and provided by Elsevier - Publisher Connector J. PecaricÏ and V.Ï Simic  Faculty of Textile Technology, Uni¨ersity of Zagreb, Pierottije¨a 6, 11000, Zagreb, Croatia Submitted by A. M. Fink Received March 25, 1997 A generalization is given of the extension of Hadamard's inequality to r-convex functions. A corresponding generalization of the Fink]Mond]PecaricÏ inequalities for r-convex functions is established. Q 1998 Academic Press 1. INTRODUCTION One of the most fundamental inequalities for convex functions is that associated with the name of Hadamard. This states that if f: wxa, b ª R is convex, then 1 b faŽ.qfb Ž. HftdtŽ. F . bya a 2 Hadamard's inequality has recently been extended in two quite different ways. Recall that the integral power mean Mp of a positive function f on 99 0022-247Xr98 $25.00 Copyright Q 1998 by Academic Press All rights of reproduction in any form reserved. 100 PEARCE, PECARICÏÂ, AND ÏÂSIMIC wxa,bis a functional given by 1 1rp ¡ b p HftŽ. dt , p/0, bya a MfpŽ.s~ Ž.1.1 1 b expH ln ftdtŽ. , ps0. ¢ bya a Further, the extended logarithmic mean Lp of two positive numbers a, b is given for a s b by LapŽ.,asaand for a / b by p 1 p 1 1rp ¡ b q y a q , p / y1,0, Ž.Ž.p q 1 b y a bya LapŽ.,bs~ , psy1, ln b y ln a 1Ž.b1 1bbry , ps0. -
Total Jensen Divergences: Definition, Properties and K-Means++ Clustering
Total Jensen divergences: Definition, Properties and k-Means++ Clustering Frank Nielsen1 Richard Nock2 www.informationgeometry.org 1Sony Computer Science Laboratories, Inc. 2UAG-CEREGMIA September 2013 c 2013 Frank Nielsen, Sony Computer Science Laboratories, Inc. 1/19 Divergences: Distortion measures F a smooth convex function, the generator. ◮ Skew Jensen divergences: ′ Jα(p : q) = αF (p) + (1 − α)F (q) − F (αp + (1 − α)q), = (F (p)F (q))α − F ((pq)α), where (pq)γ = γp + (1 − γ)q = q + γ(p − q) and (F (p)F (q))γ = γF (p)+(1−γ)F (q)= F (q)+γ(F (p)−F (q)). ◮ Bregman divergences: B(p : q)= F (p) − F (q) − p − q, ∇F (q), lim Jα(p : q) = B(p : q), α→0 lim Jα(p : q) = B(q : p). α→1 ◮ Statistical Bhattacharrya divergence: Bhat α 1−αd ′ (p1 : p2)= − log p1(x) p2(x) ν(x)= Jα(θ1 : θ2) Z for exponential families [5]. c 2013 Frank Nielsen, Sony Computer Science Laboratories, Inc. 2/19 Geometrically designed divergences Plot of the convex generator F . F :(x, F (x)) (q, F (q)) J(p, q) (p, F (p)) tB(p : q) B(p : q) p+q q 2 p c 2013 Frank Nielsen, Sony Computer Science Laboratories, Inc. 3/19 Total Bregman divergences Conformal divergence, conformal factor ρ: D′(p : q)= ρ(p, q)D(p : q) plays the rˆole of “regularizer” [8] Invariance by rotation of the axes of the design space B(p : q) tB(p : q) = = ρB (q)B(p : q), 1+ ∇F (q), ∇F (q) 1 ρB (q) = p . -
Probabilistic Models for Shapes As Continuous Curves
J Math Imaging Vis (2009) 33: 39–65 DOI 10.1007/s10851-008-0104-3 Probabilistic Models for Shapes as Continuous Curves Jeong-Gyoo Kim · J. Alison Noble · J. Michael Brady Published online: 23 July 2008 © Springer Science+Business Media, LLC 2008 Abstract We develop new shape models by defining a stan- 1 Introduction dard shape from which we can explain shape deformation and variability. Currently, planar shapes are modelled using Our work contributes to shape analysis, particularly in med- a function space, which is applied to data extracted from ical image analysis, by defining a standard representation images. We regard a shape as a continuous curve and identi- of shape. Specifically we develop new mathematical mod- fied on the Wiener measure space whereas previous methods els of planar shapes. Our aim is to develop a standard rep- have primarily used sparse sets of landmarks expressed in a resentation of anatomical or biological shapes that can be Euclidean space. The average of a sample set of shapes is used to explain shape variations, whether in normal subjects defined using measurable functions which treat the Wiener or abnormalities due, for example, to disease. In addition, measure as varying Gaussians. Various types of invariance we propose a quasi-score that measures global deformation of our formulation of an average are examined in regard to and provides a generic way to compare statistical methods practical applications of it. The average is examined with of shape analysis. relation to a Fréchet mean in order to establish its valid- As D’Arcy Thompson pointed in [50], there is an impor- ity. -
An Exposition on Means Mabrouck K
Louisiana State University LSU Digital Commons LSU Master's Theses Graduate School 2004 Which mean do you mean?: an exposition on means Mabrouck K. Faradj Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_theses Part of the Applied Mathematics Commons Recommended Citation Faradj, Mabrouck K., "Which mean do you mean?: an exposition on means" (2004). LSU Master's Theses. 1852. https://digitalcommons.lsu.edu/gradschool_theses/1852 This Thesis is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Master's Theses by an authorized graduate school editor of LSU Digital Commons. For more information, please contact [email protected]. WHICH MEAN DO YOU MEAN? AN EXPOSITION ON MEANS A Thesis Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Master of Science in The Department of Mathematics by Mabrouck K. Faradj B.S., L.S.U., 1986 M.P.A., L.S.U., 1997 August, 2004 Acknowledgments This work was motivated by an unpublished paper written by Dr. Madden in 2000. This thesis would not be possible without contributions from many people. To every one who contributed to this project, my deepest gratitude. It is a pleasure to give special thanks to Professor James J. Madden for helping me complete this work. This thesis is dedicated to my wife Marianna for sacrificing so much of her self so that I may realize my dreams.