1 History of Probability (Part 3)
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AND MARIO V. W ¨UTHRICH† This Year We Celebrate the 300Th
BERNOULLI’S LAW OF LARGE NUMBERS BY ∗ ERWIN BOLTHAUSEN AND MARIO V. W UTHRICH¨ † ABSTRACT This year we celebrate the 300th anniversary of Jakob Bernoulli’s path-breaking work Ars conjectandi, which appeared in 1713, eight years after his death. In Part IV of his masterpiece, Bernoulli proves the law of large numbers which is one of the fundamental theorems in probability theory, statistics and actuarial science. We review and comment on his original proof. KEYWORDS Bernoulli, law of large numbers, LLN. 1. INTRODUCTION In a correspondence, Jakob Bernoulli writes to Gottfried Wilhelm Leibniz in October 1703 [5]: “Obwohl aber seltsamerweise durch einen sonderbaren Na- turinstinkt auch jeder D¨ummste ohne irgend eine vorherige Unterweisung weiss, dass je mehr Beobachtungen gemacht werden, umso weniger die Gefahr besteht, dass man das Ziel verfehlt, ist es doch ganz und gar nicht Sache einer Laienun- tersuchung, dieses genau und geometrisch zu beweisen”, saying that anyone would guess that the more observations we have the less we can miss the target; however, a rigorous analysis and proof of this conjecture is not trivial at all. This extract refers to the law of large numbers. Furthermore, Bernoulli expresses that such thoughts are not new, but he is proud of being the first one who has given a rigorous mathematical proof to the statement of the law of large numbers. Bernoulli’s results are the foundations of the estimation and prediction theory that allows one to apply probability theory well beyond combinatorics. The law of large numbers is derived in Part IV of his centennial work Ars conjectandi, which appeared in 1713, eight years after his death (published by his nephew Nicolaus Bernoulli), see [1]. -
The Interpretation of Probability: Still an Open Issue? 1
philosophies Article The Interpretation of Probability: Still an Open Issue? 1 Maria Carla Galavotti Department of Philosophy and Communication, University of Bologna, Via Zamboni 38, 40126 Bologna, Italy; [email protected] Received: 19 July 2017; Accepted: 19 August 2017; Published: 29 August 2017 Abstract: Probability as understood today, namely as a quantitative notion expressible by means of a function ranging in the interval between 0–1, took shape in the mid-17th century, and presents both a mathematical and a philosophical aspect. Of these two sides, the second is by far the most controversial, and fuels a heated debate, still ongoing. After a short historical sketch of the birth and developments of probability, its major interpretations are outlined, by referring to the work of their most prominent representatives. The final section addresses the question of whether any of such interpretations can presently be considered predominant, which is answered in the negative. Keywords: probability; classical theory; frequentism; logicism; subjectivism; propensity 1. A Long Story Made Short Probability, taken as a quantitative notion whose value ranges in the interval between 0 and 1, emerged around the middle of the 17th century thanks to the work of two leading French mathematicians: Blaise Pascal and Pierre Fermat. According to a well-known anecdote: “a problem about games of chance proposed to an austere Jansenist by a man of the world was the origin of the calculus of probabilities”2. The ‘man of the world’ was the French gentleman Chevalier de Méré, a conspicuous figure at the court of Louis XIV, who asked Pascal—the ‘austere Jansenist’—the solution to some questions regarding gambling, such as how many dice tosses are needed to have a fair chance to obtain a double-six, or how the players should divide the stakes if a game is interrupted. -
Tercentennial Anniversary of Bernoulli's Law of Large Numbers
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 50, Number 3, July 2013, Pages 373–390 S 0273-0979(2013)01411-3 Article electronically published on March 28, 2013 TERCENTENNIAL ANNIVERSARY OF BERNOULLI’S LAW OF LARGE NUMBERS MANFRED DENKER 0 The importance and value of Jacob Bernoulli’s work was eloquently stated by Andre˘ı Andreyevich Markov during a speech presented to the Russian Academy of Science1 on December 1, 1913. Marking the bicentennial anniversary of the Law of Large Numbers, Markov’s words remain pertinent one hundred years later: In concluding this speech, I return to Jacob Bernoulli. His biogra- phers recall that, following the example of Archimedes he requested that on his tombstone the logarithmic spiral be inscribed with the epitaph Eadem mutata resurgo. This inscription refers, of course, to properties of the curve that he had found. But it also has a second meaning. It also expresses Bernoulli’s hope for resurrection and eternal life. We can say that this hope is being realized. More than two hundred years have passed since Bernoulli’s death but he lives and will live in his theorem. Indeed, the ideas contained in Bernoulli’s Ars Conjectandi have impacted many mathematicians since its posthumous publication in 1713. The twentieth century, in particular, has seen numerous advances in probability that can in some way be traced back to Bernoulli. It is impossible to survey the scope of Bernoulli’s influence in the last one hundred years, let alone the preceding two hundred. It is perhaps more instructive to highlight a few beautiful results and avenues of research that demonstrate the lasting effect of his work. -
The Bernoullis and the Harmonic Series
The Bernoullis and The Harmonic Series By Candice Cprek, Jamie Unseld, and Stephanie Wendschlag An Exciting Time in Math l The late 1600s and early 1700s was an exciting time period for mathematics. l The subject flourished during this period. l Math challenges were held among philosophers. l The fundamentals of Calculus were created. l Several geniuses made their mark on mathematics. Gottfried Wilhelm Leibniz (1646-1716) l Described as a universal l At age 15 he entered genius by mastering several the University of different areas of study. Leipzig, flying through l A child prodigy who studied under his father, a professor his studies at such a of moral philosophy. pace that he completed l Taught himself Latin and his doctoral dissertation Greek at a young age, while at Altdorf by 20. studying the array of books on his father’s shelves. Gottfried Wilhelm Leibniz l He then began work for the Elector of Mainz, a small state when Germany divided, where he handled legal maters. l In his spare time he designed a calculating machine that would multiply by repeated, rapid additions and divide by rapid subtractions. l 1672-sent form Germany to Paris as a high level diplomat. Gottfried Wilhelm Leibniz l At this time his math training was limited to classical training and he needed a crash course in the current trends and directions it was taking to again master another area. l When in Paris he met the Dutch scientist named Christiaan Huygens. Christiaan Huygens l He had done extensive work on mathematical curves such as the “cycloid”. -
A Tricentenary History of the Law of Large Numbers
Bernoulli 19(4), 2013, 1088–1121 DOI: 10.3150/12-BEJSP12 A Tricentenary history of the Law of Large Numbers EUGENE SENETA School of Mathematics and Statistics FO7, University of Sydney, NSW 2006, Australia. E-mail: [email protected] The Weak Law of Large Numbers is traced chronologically from its inception as Jacob Bernoulli’s Theorem in 1713, through De Moivre’s Theorem, to ultimate forms due to Uspensky and Khinchin in the 1930s, and beyond. Both aspects of Jacob Bernoulli’s Theorem: 1. As limit theorem (sample size n →∞), and: 2. Determining sufficiently large sample size for specified precision, for known and also unknown p (the inversion problem), are studied, in frequentist and Bayesian settings. The Bienaym´e–Chebyshev Inequality is shown to be a meeting point of the French and Russian directions in the history. Particular emphasis is given to less well-known aspects especially of the Russian direction, with the work of Chebyshev, Markov (the organizer of Bicentennial celebrations), and S.N. Bernstein as focal points. Keywords: Bienaym´e–Chebyshev Inequality; Jacob Bernoulli’s Theorem; J.V. Uspensky and S.N. Bernstein; Markov’s Theorem; P.A. Nekrasov and A.A. Markov; Stirling’s approximation 1. Introduction 1.1. Jacob Bernoulli’s Theorem Jacob Bernoulli’s Theorem was much more than the first instance of what came to be know in later times as the Weak Law of Large Numbers (WLLN). In modern notation Bernoulli showed that, for fixed p, any given small positive number ε, and any given large positive number c (for example c = 1000), n may be specified so that: arXiv:1309.6488v1 [math.ST] 25 Sep 2013 X 1 P p >ε < (1) n − c +1 for n n0(ε,c). -
Jakob Bernoulli on the Law of Large Numbers
Jakob Bernoulli On the Law of Large Numbers Translated into English by Oscar Sheynin Berlin 2005 Jacobi Bernoulli Ars Conjectandi Basileae, Impensis Thurnisiorum, Fratrum, 1713 Translation of Pars Quarta tradens Usum & Applicationem Praecedentis Doctrinae in Civilibus, Moralibus & Oeconomicis [The Art of Conjecturing; Part Four showing The Use and Application of the Previous Doctrine to Civil, Moral and Economic Affairs ] ISBN 3-938417-14-5 Contents Foreword 1. The Art of Conjecturing and Its Contents 2. The Art of Conjecturing, Part 4 2.1. Randomness and Necessity 2.2. Stochastic Assumptions and Arguments 2.3. Arnauld and Leibniz 2.4. The Law of Large Numbers 3. The Translations Jakob Bernoulli. The Art of Conjecturing; Part 4 showing The Use and Application of the Previous Doctrine to Civil, Moral and Economic Affairs Chapter 1. Some Preliminary Remarks about Certainty, Probability, Necessity and Fortuity of Things Chapter 2. On Arguments and Conjecture. On the Art of Conjecturing. On the Grounds for Conjecturing. Some General Pertinent Axioms Chapter 3. On Arguments of Different Kinds and on How Their Weights Are Estimated for Calculating the Probabilities of Things Chapter 4. On a Two-Fold Method of Investigating the Number of Cases. What Ought To Be Thought about Something Established by Experience. A Special Problem Proposed in This Case, etc Chapter 5. Solution of the Previous Problem Notes References Foreword 1. The Art of Conjecturing and Its Contents Jakob Bernoulli (1654 – 1705) was a most eminent mathematician, mechanician and physicist. His Ars Conjectandi (1713) (AC) was published posthumously with a Foreword by his nephew, Niklaus Bernoulli (English translation: David (1962, pp. -
Reliable Reasoning”
Abstracta SPECIAL ISSUE III, pp. 10 – 17, 2009 COMMENTS ON HARMAN AND KULKARNI’S “RELIABLE REASONING” Glenn Shafer Gil Harman and Sanjeev Kulkarni have written an enjoyable and informative book that makes Vladimir Vapnik’s ideas accessible to a wide audience and explores their relevance to the philosophy of induction and reliable reasoning. The undertaking is important, and the execution is laudable. Vapnik’s work with Alexey Chervonenkis on statistical classification, carried out in the Soviet Union in the 1960s and 1970s, became popular in computer science in the 1990s, partly as the result of Vapnik’s books in English. Vapnik’s statistical learning theory and the statistical methods he calls support vector machines now dominate machine learning, the branch of computer science concerned with statistical prediction, and recently (largely after Harman and Kulkarni completed their book) these ideas have also become well known among mathematical statisticians. A century ago, when the academic world was smaller and less specialized, philosophers, mathematicians, and scientists interested in probability, induction, and scientific methodology talked with each other more than they do now. Keynes studied Bortkiewicz, Kolmogorov studied von Mises, Le Dantec debated Borel, and Fisher debated Jeffreys. Today, debate about probability and induction is mostly conducted within more homogeneous circles, intellectual communities that sometimes cross the boundaries of academic disciplines but overlap less in their discourse than in their membership. Philosophy of science, cognitive science, and machine learning are three of these communities. The greatest virtue of this book is that it makes ideas from these three communities confront each other. In particular, it looks at how Vapnik’s ideas in machine learning can answer or dissolve questions and puzzles that have been posed by philosophers. -
18.600: Lecture 31 .1In Strong Law of Large Numbers and Jensen's
18.600: Lecture 31 Strong law of large numbers and Jensen's inequality Scott Sheffield MIT Outline A story about Pedro Strong law of large numbers Jensen's inequality Outline A story about Pedro Strong law of large numbers Jensen's inequality I One possibility: put the entire sum in government insured interest-bearing savings account. He considers this completely risk free. The (post-tax) interest rate equals the inflation rate, so the real value of his savings is guaranteed not to change. I Riskier possibility: put sum in investment where every month real value goes up 15 percent with probability :53 and down 15 percent with probability :47 (independently of everything else). I How much does Pedro make in expectation over 10 years with risky approach? 100 years? Pedro's hopes and dreams I Pedro is considering two ways to invest his life savings. I Riskier possibility: put sum in investment where every month real value goes up 15 percent with probability :53 and down 15 percent with probability :47 (independently of everything else). I How much does Pedro make in expectation over 10 years with risky approach? 100 years? Pedro's hopes and dreams I Pedro is considering two ways to invest his life savings. I One possibility: put the entire sum in government insured interest-bearing savings account. He considers this completely risk free. The (post-tax) interest rate equals the inflation rate, so the real value of his savings is guaranteed not to change. I How much does Pedro make in expectation over 10 years with risky approach? 100 years? Pedro's hopes and dreams I Pedro is considering two ways to invest his life savings. -
Stochastic Models Laws of Large Numbers and Functional Central
This article was downloaded by: [Stanford University] On: 20 July 2010 Access details: Access Details: [subscription number 731837804] Publisher Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37- 41 Mortimer Street, London W1T 3JH, UK Stochastic Models Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713597301 Laws of Large Numbers and Functional Central Limit Theorems for Generalized Semi-Markov Processes Peter W. Glynna; Peter J. Haasb a Department of Management Science and Engineering, Stanford University, Stanford, California, USA b IBM Almaden Research Center, San Jose, California, USA To cite this Article Glynn, Peter W. and Haas, Peter J.(2006) 'Laws of Large Numbers and Functional Central Limit Theorems for Generalized Semi-Markov Processes', Stochastic Models, 22: 2, 201 — 231 To link to this Article: DOI: 10.1080/15326340600648997 URL: http://dx.doi.org/10.1080/15326340600648997 PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. -
On the Law of the Iterated Logarithm for L-Statistics Without Variance
Bulletin of the Institute of Mathematics Academia Sinica (New Series) Vol. 3 (2008), No. 3, pp. 417-432 ON THE LAW OF THE ITERATED LOGARITHM FOR L-STATISTICS WITHOUT VARIANCE BY DELI LI, DONG LIU AND ANDREW ROSALSKY Abstract Let {X, Xn; n ≥ 1} be a sequence of i.i.d. random vari- ables with distribution function F (x). For each positive inte- ger n, let X1:n ≤ X2:n ≤ ··· ≤ Xn:n be the order statistics of X1, X2, · · · , Xn. Let H(·) be a real Borel-measurable function de- fined on R such that E|H(X)| < ∞ and let J(·) be a Lipschitz function of order one defined on [0, 1]. Write µ = µ(F,J,H) = ← 1 n i E L : (J(U)H(F (U))) and n(F,J,H) = n Pi=1 J n H(Xi n), n ≥ 1, where U is a random variable with the uniform (0, 1) dis- ← tribution and F (t) = inf{x; F (x) ≥ t}, 0 <t< 1. In this note, the Chung-Smirnov LIL for empirical processes and the Einmahl- Li LIL for partial sums of i.i.d. random variables without variance are used to establish necessary and sufficient conditions for having L with probability 1: 0 < lim supn→∞ pn/ϕ(n) | n(F,J,H) − µ| < ∞, where ϕ(·) is from a suitable subclass of the positive, non- decreasing, and slowly varying functions defined on [0, ∞). The almost sure value of the limsup is identified under suitable con- ditions. Specializing our result to ϕ(x) = 2(log log x)p,p > 1 and to ϕ(x) = 2(log x)r,r > 0, we obtain an analog of the Hartman- Wintner-Strassen LIL for L-statistics in the infinite variance case. -
The Law of Large Numbers and the Monte-Carlo Method
Lecture 17: The Law of Large Numbers and the Monte-Carlo method The Law of Large numbers Suppose we perform an experiment and a measurement encoded in the random variable X and that we repeat this experiment n times each time in the same conditions and each time independently of each other. We thus obtain n independent copies of the random variable X which we denote X1;X2; ··· ;Xn Such a collection of random variable is called a IID sequence of random variables where IID stands for independent and identically distributed. This means that the random variables Xi have the same probability distribution. In particular they have all the same means and variance 2 E[Xi] = µ ; var(Xi) = σ ; i = 1; 2; ··· ; n Each time we perform the experiment n tiimes, the Xi provides a (random) measurement and if the average value X1 + ··· + Xn n is called the empirical average. The Law of Large Numbers states for large n the empirical average is very close to the expected value µ with very high probability Theorem 1. Let X1; ··· ;Xn IID random variables with E[Xi] = µ and var(Xi) for all i. Then we have 2 X1 + ··· Xn σ P − µ ≥ ≤ n n2 In particular the right hand side goes to 0 has n ! 1. Proof. The proof of the law of large numbers is a simple application from Chebyshev X1+···Xn inequality to the random variable n . Indeed by the properties of expectations we have X + ··· X 1 1 1 E 1 n = E [X + ··· X ] = (E [X ] + ··· E [X ]) = nµ = µ n n 1 n n 1 n n For the variance we use that the Xi are independent and so we have X + ··· X 1 1 σ2 var 1 n = var (X + ··· X ]) = (var(X ) + ··· + var(X )) = n n2 1 n n2 1 n n 1 By Chebyshev inequality we obtain then 2 X1 + ··· Xn σ P − µ ≥ ≤ n n2 Coin flip I: Suppose we flip a fair coin 100 times. -
Laws of Large Numbers in Stochastic Geometry with Statistical Applications
Bernoulli 13(4), 2007, 1124–1150 DOI: 10.3150/07-BEJ5167 Laws of large numbers in stochastic geometry with statistical applications MATHEW D. PENROSE Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, United Kingdom. E-mail: [email protected] Given n independent random marked d-vectors (points) Xi distributed with a common density, define the measure νn = i ξi, where ξi is a measure (not necessarily a point measure) which stabilizes; this means that ξi is determined by the (suitably rescaled) set of points near Xi. For d bounded test functions fPon R , we give weak and strong laws of large numbers for νn(f). The general results are applied to demonstrate that an unknown set A in d-space can be consistently estimated, given data on which of the points Xi lie in A, by the corresponding union of Voronoi cells, answering a question raised by Khmaladze and Toronjadze. Further applications are given concerning the Gamma statistic for estimating the variance in nonparametric regression. Keywords: law of large numbers; nearest neighbours; nonparametric regression; point process; random measure; stabilization; Voronoi coverage 1. Introduction Many interesting random variables in stochastic geometry arise as sums of contributions from each point of a point process Xn comprising n independent random d-vectors Xi, 1 ≤ i ≤ n, distributed with common density function. General limit theorems, including laws of large numbers (LLNs), central limit theorems and large deviation principles, have been obtained for such variables, based on a notion of stabilization (local dependence) of the contributions; see [16, 17, 18, 20].