Burgin M. Hypernumbers and Extrafunctions.. Extending The
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
P</Emphasis>-Variation, Calculus, and Index Estimation
Lithuanian Mathematical Journal, Vol. 46, No. 1, 2006 ROUGH FUNCTIONS: p-VARIATION, CALCULUS, AND INDEX ESTIMATION R. Norvaiša Institute of Mathematics and Informatics, Akademijos 4, LT-08663 Vilnius; Vilnius University, Naugarduko 24, LT-03225 Vilnius, Lithuania (e-mail: [email protected]) Abstract. In this paper, we give an overview of several dissipated results on the p-variation property of a function presented in a suitable way. More specifically, we attempt to show: (1) usefulness of this property in a calculus of rough functions; (2) a relatively thorough knowledge of the p-variation property of a sample function of basic stochastic processes; and (3) an almost unexplored area of statistical analysis seeking to estimate the p-variation index. Keywords: -variation, interval functions, refinement Young–Stieltjes integral, Kolmogorov integral, product integral, Nemyt- skii operator, integral equations, sample functions of stochastic processes, p-variation index, Orey index, oscillation summing index. 1. INTRODUCTION The p-variation is a generalization of the total variation of a function. More specifically, for 0 <p<∞ and −∞ <a<b<+∞,thep-variation of a real-valued function f on an interval [a,b] is vp(f ) := vp f ;[a,b] n p := sup f(ti) − f(ti−1) : a = t0 <t1 < ···<tn = b, n 1 . (1) i=1 We say that f has bounded p-variation if vp(f ) < ∞. Thus, a function has bounded total variation if it has bounded 1-variation. A regulated function f is called rough if it has infinite 1-variation, that is, if v1(f ) =+∞. The p-variation gives a convenient way to measure a degree of roughness. -
Appendix Nonatomic Measure Spaces
Appendix Nonatomic Measure Spaces In this appendix we collect some facts about finite measure spaces without (and with) atoms, which are applied in Chapter 7. Let (Ω, , µ) be a σ-finite measure space. Then an atom of µ is a set A with Sµ(A) > 0 such that for all C with C A, either µ(C) = 0 or µ(C∈) S = µ(A). By σ-finiteness, we have µ∈(A S) < + .(⊂Ω, , µ) or µ is called nonatomic if it has no atoms. ∞ S Proposition A.1. Let (Ω, , µ) be a nonatomic finite measure space with µ(Ω) > 0. Then for any cSwith 0 < c < µ(Ω), there is an A with µ(A) = c. ∈ S Proof. We can assume that µ(Ω) = 1. It will first be shown that for some C , 1/3 µ(C) 2/3. Suppose not. Let p := sup µ(B): µ(B) < 1/3 1/3.∈ SThen p≤ > 0 since≤Ω is not an atomand if 2/3 < µ({D) < 1, let B := Ω }D ≤. Take B with µ(B ) p. Let E := n B . It will be shown by induction\ n ∈ S n ↑ n j=1 j that µ(En) < 1/3 for all n. This is true for n = 1. Assuming it holds for a given n, we have µ(E ) = µ(E BS ) < (1/3)+(1/3) = 2/3. Thus by the n+1 n ∪ n+1 assumption that µ takes no values in [1/3, 2/3], µ(En+1) < 1/3, completing ∞ the induction. Let E := n=1 Bn. -
Computing Aspects of Problems in Non.Linear
COMPUTING ASPECTS OF PROBLEMS IN NON.LINEAR PREDICTION AND FILTERING by I. G. CUMMING A thesis submitted for the degree of Ph.D. in Engineering Centre for Computing and Automation, Imperial College, University of London, London, S.W.7. MAY 1967 _ 2 - ABSTRACT This thesis discusses some of the computational problems arising in the application of modern stochastic control theory. We deal with continuous time systems where two typical problems are the prediction of the future statistical behaviour of systems and the synthesis of systems designed from stochastic theory such as filters. In each case, computational problems occur if the system is non-linear or non-Gaussian. The non-linear prediction problem involves solving a parabolic partial differential equation, the Fokker-Planck equation, and we dis- cuss two numerical methods of solving this equation. Finding that these methods can only handle a restricted class of low-dimensional systems, we study Monte Carlo methods in the hopes of finding a more general solution procedure. We find these to be successful if we allow for accuracy limitations, and find that the theory of the Fokker- Planck equation can be extended to include the Monte Carlo solution of a wider class of parabolic equations than previous methods would accommodate. Monte Carlo methods involve the simulation of a stochastic system on a computer, and as the problem of synthesising a given system is the same as simulating it on a computer, the rest of the thesis centres around the theoretical and practical aspects of simulation techniques. We find in each case that the system we must simulate is a continuous Markov (diffusion) process, and that diffusion processes have properties which distinguish them from any process which can ,be constructed on a computer or in the physical world (we call these physical processes). -
FINITE ADDITIVITY VERSUS COUNTABLE ADDITIVITY: De FINETTI and SAVAGE
FINITE ADDITIVITY VERSUS COUNTABLE ADDITIVITY: De FINETTI AND SAVAGE N. H. BINGHAM Electronic J. History of Probability and Statistics 6.1 (2010), 33p R´esum´e L'arri`ere-planhistorique, d'abord de l'additivit´ed´enombrable et puis de l'additivit´efinie, est ´etudi´eici. Nous discutons le travail de de Finetti (en particulier, son livre posthume, de Finetti (2008)), mais aussi les travaux de Savage. Tous deux sont surtout (re)connus pour leurs contributions au domaine des statistiques; nous nous int´eressonsici `aleurs apports du point de vue de la th´eoriedes probabilit´es.Le probl`emede mesure est discut´e{ la possibilit´ed'´etendreune mesure `a tout sous-ensemble d'un espace de proba- bilit´e. La th´eoriedes jeux de hasard est ensuite pr´esent´eepour illustrer les m´eritesrelatifs de l'additivit´efinie et d´enombrable. Puis, nous consid´eronsla coh´erencedes d´ecisions,o`uun troisi`emecandidat apparait { la non-additivit´e. Nous ´etudionsalors l’influence de diff´erents choix d'axiomes de la th´eoriedes ensembles. Nous nous adressons aux six raisons avanc´espar Seidenfeld (2001) `al'appui de l'additivit´efinie, et faisons la critique des plusi`eresapproches `a la fr´equencelimite. Abstract The historical background of first countable additivity, and then finite ad- ditivity, in probability theory is reviewed. We discuss the work of the most prominent advocate of finite additivity, de Finetti (in particular, his posthu- mous book de Finetti (2008)), and also the work of Savage. Both were most noted for their contributions to statistics; our focus here is more from the point of view of probability theory. -
Author Index ______
______AUTHOR INDEX ______ a space; Homology group; Infinite Arellano, E. Ramirez de see: Ramirez de form in logarithms; Siegel theorem; dimensional space; Local decompo Arellano, E. Transcendental number sition; Metrizable space; Pontryagin __A __ Argand, J .R. see: Arithmetic; Complex Ball, R. see: Helical calculus duality; Projection spectrum; Suslin number; Imaginary number Balusabramanian, R. see: Waring prob theorem; Topological space; Width Aristotle see: Modal logic; Space-time lem Abel, N.H. see: Abel differential Alexander, J.W. see: Alexander dual Arkhangel'skil, A.V. see: Feathering Banach, S. see: Area; Banach indica equation; Abel-Goncharov prob ity; Alexander invariants; Duality; Arno I'd, V.I. see: Composite function; trix; Banach-Mazur functional; Ba lem; Abel problem; Abel theorem; Homology group; Jordan theorem; Quasi-periodic motion nach space; Banach-Steinhaus the Abel transformation; Abelian group; Knot theory; Manifold; Pontryagin Arsenin, V. Ya. see: Descriptive set the orem; Completely-continuous oper Abelian integral; Abelian variety; duality; Reaction-diffusion equation; ory ator; Contracting-mapping princi Algebra; Algebraic equation; Alge Topology of imbeddings Artin, E. see: Abstract algebraic ge ple; Franklin system; Hahn-Banach braic function; Algebraic geometry; Alexandrov, A.B. see: Boundary prop ometry; Algebra; Algebraic number theorem; Lacunary system; Linear Binomial series; Convergence, types erties of analytic functions theory; Alternative rings and alge operator; Luzin N -property; Non of; Elliptic function; Finite group; Alling, N.L. see: Surreal numbers bras; Commutative algebra; Con differentiable function; Nuclear op Galois theory; Group; Interpola Almgren, F.J. see: Plateau problem, gruence modulo a prime number; erator; Open-mapping theorem; Or tion; Inversion of an elliptic integral; multi-dimensional Divisorial ideal; Foundations of ge thogonal series; Tonelli plane vari Jacobi elliptic functions; Liouville Alvarez-Gaume, L. -
Bibliographical and Historical Comments
Bibliographical and Historical Comments One gets a strange feeling having seen the same drawings as if drawn by the same hand in the works of four schol- ars that worked completely independently of each other. An involuntary thought comes that such a striking, myste- rious activity of mankind, lasting several thousand years, cannot be occasional and must have a certain goal. Having acknowledged this, we come by necessity to the question: what is this goal? I.R. Shafarevich. On some tendencies of the develop- ment of mathematics. However, also in my contacts with the American Shake- speare scholars I confined myself to the concrete problems of my research: dating, identification of prototypes, direc- tions of certain allusions. I avoided touching the problem of personality of the Great Bard, the “Shakespeare prob- lem”; neither did I hear those scholars discussing such a problem between themselves. I.M. Gililov. A play about William Shakespeare or the Mystery of the Great Phoenix. The extensive bibliography in this book covers, however, only a small portion of the existing immense literature on measure theory; in particular, many authors are represented by a minimal number of their most character- istic works. Guided by the proposed brief comments and this incomplete list, the reader, with help of modern electronic data-bases, can considerably en- large the bibliography. The list of books is more complete (although it cannot pretend to be absolutely complete). For the reader’s convenience, the bibli- ography includes the collected (or selected) works of A.D. Alexandrov [15], R. Baire [47], S. Banach [56], E. -
School of Economics, Mathematics and Statistics Birkbeck College
MATHEMATICAL METHODS I MSC FINANCIAL ENGINEERING RAYMOND BRUMMELHUIS School of Economics, Mathematics and Statistics Birkbeck College Autumn Term 2004-2005 1. Introduction Market prices of liquidly traded financial assets depend on a huge number of factors: macro-economic ones like interest rates, inflation, balanced or unbalanced budgets, micro-economic and business-specific factors like flexibility of labor markets, sale numbers, investments, and also on more elusive psychological factors like the aggregate expec- tations, illusions and disillusions of the various market players, both professional ones (stock brokers, market makers, fund managers, banks and institutional investors like pension funds), and humble private in- vestors (like professors of finance, seeking to complement their modest revenue). Although many people have dreamed (and still do dream) of all-encompassing deterministic models for, for example, stock prices, the number of potentially influencing factors seems to high to realis- tically hope for such a thing. Already in the first half of the 20-th century researchers began to realize that a statistical approach to fi- nancial markets might be the best one. This started in fact right at the beginning of the century, in 1900, when a young French mathe- matician, Louis Bachelier, defended a thesis at the Sorbonne in Paris, France, on a probabilistic model of the French bourse. In his thesis, he developed the first mathematical model of what later came to be known as ”Brownian motion”, with the specific aim of giving a sta- tistical description of the prices of financial transactions on the Paris stock market. The phrase with which he ended his thesis, that ”the bourse, without knowing it, follows the laws of probability”, are still a guiding principle of modern quantitative finance. -
Albanese C., Campolieti G. Advanced Derivatives Pricing and Risk
Advanced Derivatives Pricing and Risk Management ThisPageIntentionallyLeftBlank ADVANCED DERIVATIVES PRICING AND RISK MANAGEMENT Theory, Tools and Hands-On Programming Application Claudio Albanese and Giuseppe Campolieti AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Elsevier Academic Press 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, California 92101-4495, USA 84 Theobald’s Road, London WC1X 8RR, UK This book is printed on acid-free paper. Copyright © 2006, Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: (+44) 1865 843830, fax: (+44) 1865 853333, e-mail: [email protected]. You may also complete your request online via the Elsevier homepage (http://elsevier.com), by selecting “Customer Support” and then “Obtaining Permissions.” Library of Congress Cataloging-in-Publication Data Application Submitted British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 13: 978-0-12-047682-4 ISBN 10: 0-12-047682-7 The content of this book is presented solely for educational purposes. Neither the authors nor Elsevier/Academic Press accept any responsibility or liability for loss or damage arising from any application of the material, methods or ideas, included in any part of the theory or software contained in this book. -
Finite Additivity Versus Countable Additivity N
Journ@l Electronique d’Histoire des Probabilités et de la Statistique Electronic Journ@l for History of Probability and Statistics Vol 6, n°1; Juin/June 2010 www.jehps.net FINITE ADDITIVITY VERSUS COUNTABLE ADDITIVITY N. H. BINGHAM1 R´esum´e L’arri`ere-plan historique, d’abord de l’additivit´ed´enombrable puis de l’additivit´e finie, est ´etudi´eici. Nous discutons le travail de de Finetti, mais aussi les travaux de Savage. Tous deux sont surtout (re)connus pour leurs contributions au domaine des statistiques; nous nous int´eressons ici `aleurs apports du point de vue de la th´eorie des probabilit´es.Le probl`emede mesure est discut´e– la possibilit´ed’´etendre une mesure `a tout sous-ensemble d’un espace de probabilit´e. La th´eorie des jeux de hasard est ensuite pr´esent´eepour illustrer les m´erites relatifs de l’additivit´efinie et d´enombrable. Puis, nous consid´erons la coh´erence des d´ecisions, o`uun troisi`eme candidat apparait – la non-additivit´e. Nous ´etudions alors l’influence de diff´erents choix d’axiomes de la th´eorie des ensembles. Abstract The historical background of first countable additivity, and then finite additivity, in probability theory is reviewed. We discuss the work of the most prominent advocate of finite additivity, de Finetti, and also the work of Savage. Both were most noted for their contributions to statistics; our focus here is more from the point of view of probability theory. The problem of measure is then discussed – the possibility of extending a measure to all subsets of a probability space.