The Aftermath of Abraham De Moivre's Doctrine of Chances and Annuities on Lives in 18Th-Century Europe

Total Page:16

File Type:pdf, Size:1020Kb

The Aftermath of Abraham De Moivre's Doctrine of Chances and Annuities on Lives in 18Th-Century Europe Capítulo 14 The aftermath of Abraham de Moivre's Doctrine of Chances and Annuities on Lives in 18th-century Europe Ivo S c h n e id e r Miinchner Zertrum fur Wissenschafts-und T echnikgeschichte Deutches Museum Some biographical data of De Moivre' Abraham Moivre was bom in May 26, 1667 as the son of a Protestant surgeon in Vitry-le- François in the Champagne. He spent the first 20 years of his life in France where he was educated in different Huguenot institutions until they were closed in the early 1680s. Quite early he had turned to mathematics. At 16 he had studied amongst other things Huygens' tract "De ratiociniis in ludo aleae". In Paris in the 1680s he was taught by the private teacher of mathematics Jacques Ozanam, who might have offered to Moivre a model for how to make his living when he had to support himself shortly afterwards. After the revocation of the Edict of Nantes in 1685 hundreds of thousands of Huguenots left France. Amongst them was Moivre who went to England together with his brother Daniel in December 1687. In England he began his occupation as a tutor in mathematics. Here he added a "de" to his name. De Moivre mastered Newton's Principia from 1687 very early and became a true and loyal Newtonian. In 1692 he met Halley and Newton and in 1697 he was chosen a Fellow of the Royal Society. He was naturalized in 1705, and, when Newton had nominated him a member of the commission to decide the priority dispute between himself and Leibniz in favour of Newton in 1712, he gave up every hope of finding a position as a university professor for mathematics on the continent. But even in England he had to learn that for him the only way to make a living was to work as a private teacher and as a consultant for problems concerning different forms of annuities. In order to attract well-to-do clients he had to make a reputation as a mathematician. He soon found out that it was much easier to do that in the calculus of games of chance than in the new field of the infinitesimal calculus. Francis Robartes, one of de Moivre's clients, draw de Moivre's attention to the first edition of 231 H istoria de la Probabilidad y la Estadística (IV) Montmort's Essay d’analyse sur les jeux de hazard from 1708, which raised de Moivre's interest in the theory of games of chance and probability. In the Philosophical Transactions for 1711 de Moivre published a longer article De mensura sortis, seu, De Probabilitate Eventuum in Ludis a Casu Fortuito Pendentibus on the subject, which was followed by his Doctrine of Chances, the first edition of which was published in 1718. A second edition from 1738 contained de Moivre's normal approximation to the binomial distribution, which he had found in 1733. The third edition from 1756 contained as a second part the Annuities on Lives, which had been published as a monograph for the first time in 1725. The Doctrine of Chances is in part the result of a competition between de Moivre on the one hand and Montmort together with Niklaus Bernoulli on the other. De Moivre claimed - very much to the annoyance of Montmort, but justified by his later work - that his representation of the solutions of the then current problems tended to be more general than those of Montmort. This holds at least for the second and third edition of the Doctrine of Chances, which offered so many new results that Montmort’s contributions to the subject fell justly into oblivion. Precursors of de Moivre Christiaan Huygens with his tract De ratiociniis in ludo aleae'1 of 1657 was the first author who acquainted de Moivre with the calculus of games of chance. In England de Moivre read a small booklet Of the Laws of Chance, or, a Method of Calculation of the Hazards of Game, published anonymously by John Arbuthnot in 1692, which was largely inspired by Huygens' tract. When in 1708 the Essai d'Analyse sur le Jeux de Hasard of the French nobleman Pierre Rémond de Montmort appeared anonymously, de Moivre hurried to read this work written in his native tongue. Edmond Halley’s "An Estimate of the Degrees of the Mortality of Mankind", published in the Philosophical Transactions for 1693'“, might have become known to de Moivre quite early, since he had met Halley in 1692 in person. When de Moivre wrote De Mensura Sortis", his first tract concerning games of chance and probability, he had no knowledge of Jakob Bernoulli's Ars Conjectandi, which was published in 1713, eight years after Bernoulli's death. In the preface of the Ars Conjectandi Nikolaus Bernoulli, Jakob Bernoulli's nephew, had asked de Montmort and de Moivre to continue his uncle’s work, the application of the calculus of probabilities "to economical and political uses". Shortly after the Ars Conjectandi the second edition of Montmort's Essai of 1714 appeared, which contained, apart from some improvements, an extension of the first edition and the correspondence between Montmort and Nikolaus Bernoulli up to the end of 1713. The long preface for the first edition of the Doctrine of Chances of 1718 shows that de Moivre understood the second edition of the Essai as a challenge to replace the solutions offered by Montmort and Nikolaus Bernoulli by his own. This is one reason why he showed no interest in continuing Jakob Bernoulli's project of applying the theory of probability to economics and politics as outlined in the Ars conjectandi. Surprisingly, de Moivre seems to have been unfamiliar with the correspondence between Pascal and Fermat from 1654 concerning games of chance, part of which was published still in the 17th centuryv, and with Blaise Pascal's Traité du triangle arithmétique published in 1665. In constrast to Huygens, Pascal and Fermat used combinatorial methods for the solution of the problems discussed by them. But even Jakob Bernoulli, who had devoted the second part of the Ars Conjectandi to combinatorics, was obviously totally ignorant of Pascal's 232 H istoria de la P robabilidad y la Estadística (IV) contributions to combinatorics. De Moivre's main contributions to stochastics De Moivre's main contributions to stochastics are contained first of all in his The Doctrine of Chances: Or, A Method for Calculating the Probability of Events in Play, with editions in 1718, 1738, and 1756 and in his Annuities on Lives, which appeared first in 1725; later editions came out in 1731, 1743, 1750, 1752 and together with the Doctrine of Chances in 1756. The Miscellanea analytica of 1730 dealt inter alia with open issues in the controversy with Montmort who, however, had died already in 1719. In a supplement to the Miscellanea analytica de Moivre had started, in competition with James Stirling, to find an approximation for n-factorial when n is large. This led in 1733 to the first formulation of the central limit theorem in the Approximatio ad Summam Terminorum Binomii (a+b)n in Seriem expansi which de Moivre declared as a private communication for some friends as distinct from a publication. The publication of this result occurred only in the second and third edition of the Doctrine of chances'4. The main achievements contained in the Doctrine are a first theory of probability in the introduction™, which contains the principal concepts like probability, conditional probability, expectation, dependent and independent events, the multiplication rule, and the binomial distribution. De Moivre had also developed algebraical and analytical tools for the theory of probability for instance a "new algebra" for the solution of the problem of coincidences, which forshadowed Boolean algebra™1, the theory of recurrent series for the solution of difference equations, and the method of generating functions. He used the method of generating functions, for example, for solving the general dicing problem of finding the number of chances to get s points with a throw of n dice each having f faceslx. De Moivre determines the number by taking the coefficient of xs in the development of ( j _ Y i * • V i=l He had developed for the solution of the problem of the duration of play the theory of what he called recurrent series, which are in modem terminology homogeneous linear difference equations with constant coefficients. De Moivre's greatest mathematical achievement is considered a form of the central limit theorem, which he found in 1733 at the age of 66. He understood his central limit theorem as a generalization and a sharpening of Bernoulli's main theorem in the fourth part of the Ars Conjectandi, which was later named the law of large numbers by Poissonx. Starting from Bernoulli's law of large numbers in the form \>P(\rn-p\<e)>j-^ where rn is the relative frequency of the occurrence of independent events E¡, i = l,...,n with P(E¡) = p, s arbitrarily small and c arbitralily large quantities in a sufficiently large number n 233 H istoria de la P robabilidad y la Estadística (IV) of trials, de Moivre found on the basis of an approximation of log n! for large n and of Bernoulli's formula for the sum of powers of integers the equivalent of 2p(\-p) 2 limP r „ ~ p \ ^ c . fe'dt. n—ko JO De Moivre calculated the numerical values 0.682.., 0.954.. and 0.998..of this integral for c = 1,2, and 3. He concluded from this result that "altho1 Chance produces Irregularities" those irregularities shall cancel out with time in order to reveal the "Order which naturally results from Original Design" and so the existence of God, "the great Maker and Govemour of all"’11.
Recommended publications
  • The Open Handbook of Formal Epistemology
    THEOPENHANDBOOKOFFORMALEPISTEMOLOGY Richard Pettigrew &Jonathan Weisberg,Eds. THEOPENHANDBOOKOFFORMAL EPISTEMOLOGY Richard Pettigrew &Jonathan Weisberg,Eds. Published open access by PhilPapers, 2019 All entries copyright © their respective authors and licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. LISTOFCONTRIBUTORS R. A. Briggs Stanford University Michael Caie University of Toronto Kenny Easwaran Texas A&M University Konstantin Genin University of Toronto Franz Huber University of Toronto Jason Konek University of Bristol Hanti Lin University of California, Davis Anna Mahtani London School of Economics Johanna Thoma London School of Economics Michael G. Titelbaum University of Wisconsin, Madison Sylvia Wenmackers Katholieke Universiteit Leuven iii For our teachers Overall, and ultimately, mathematical methods are necessary for philosophical progress. — Hannes Leitgeb There is no mathematical substitute for philosophy. — Saul Kripke PREFACE In formal epistemology, we use mathematical methods to explore the questions of epistemology and rational choice. What can we know? What should we believe and how strongly? How should we act based on our beliefs and values? We begin by modelling phenomena like knowledge, belief, and desire using mathematical machinery, just as a biologist might model the fluc- tuations of a pair of competing populations, or a physicist might model the turbulence of a fluid passing through a small aperture. Then, we ex- plore, discover, and justify the laws governing those phenomena, using the precision that mathematical machinery affords. For example, we might represent a person by the strengths of their beliefs, and we might measure these using real numbers, which we call credences. Having done this, we might ask what the norms are that govern that person when we represent them in that way.
    [Show full text]
  • The Bayesian Approach to the Philosophy of Science
    The Bayesian Approach to the Philosophy of Science Michael Strevens For the Macmillan Encyclopedia of Philosophy, second edition The posthumous publication, in 1763, of Thomas Bayes’ “Essay Towards Solving a Problem in the Doctrine of Chances” inaugurated a revolution in the understanding of the confirmation of scientific hypotheses—two hun- dred years later. Such a long period of neglect, followed by such a sweeping revival, ensured that it was the inhabitants of the latter half of the twentieth century above all who determined what it was to take a “Bayesian approach” to scientific reasoning. Like most confirmation theorists, Bayesians alternate between a descrip- tive and a prescriptive tone in their teachings: they aim both to describe how scientific evidence is assessed, and to prescribe how it ought to be assessed. This double message will be made explicit at some points, but passed over quietly elsewhere. Subjective Probability The first of the three fundamental tenets of Bayesianism is that the scientist’s epistemic attitude to any scientifically significant proposition is, or ought to be, exhausted by the subjective probability the scientist assigns to the propo- sition. A subjective probability is a number between zero and one that re- 1 flects in some sense the scientist’s confidence that the proposition is true. (Subjective probabilities are sometimes called degrees of belief or credences.) A scientist’s subjective probability for a proposition is, then, more a psy- chological fact about the scientist than an observer-independent fact about the proposition. Very roughly, it is not a matter of how likely the truth of the proposition actually is, but about how likely the scientist thinks it to be.
    [Show full text]
  • Probability and Its Applications
    Probability and its Applications Published in association with the Applied Probability Trust Editors: S. Asmussen, J. Gani, P. Jagers, T.G. Kurtz Probability and its Applications Azencott et al.: Series of Irregular Observations. Forecasting and Model Building. 1986 Bass: Diffusions and Elliptic Operators. 1997 Bass: Probabilistic Techniques in Analysis. 1995 Berglund/Gentz: Noise-Induced Phenomena in Slow-Fast Dynamical Systems: A Sample-Paths Approach. 2006 Biagini/Hu/Øksendal/Zhang: Stochastic Calculus for Fractional Brownian Motion and Applications. 2008 Chen: Eigenvalues, Inequalities and Ergodic Theory. 2005 Costa/Fragoso/Marques: Discrete-Time Markov Jump Linear Systems. 2005 Daley/Vere-Jones: An Introduction to the Theory of Point Processes I: Elementary Theory and Methods. 2nd ed. 2003, corr. 2nd printing 2005 Daley/Vere-Jones: An Introduction to the Theory of Point Processes II: General Theory and Structure. 2nd ed. 2008 de la Peña/Gine: Decoupling: From Dependence to Independence, Randomly Stopped Processes, U-Statistics and Processes, Martingales and Beyond. 1999 de la Peña/Lai/Shao: Self-Normalized Processes. 2009 Del Moral: Feynman-Kac Formulae. Genealogical and Interacting Particle Systems with Applications. 2004 Durrett: Probability Models for DNA Sequence Evolution. 2002, 2nd ed. 2008 Ethier: The Doctrine of Chances. Probabilistic Aspects of Gambling. 2010 Feng: The Poisson–Dirichlet Distribution and Related Topics. 2010 Galambos/Simonelli: Bonferroni-Type Inequalities with Equations. 1996 Gani (ed.): The Craft of Probabilistic Modelling. A Collection of Personal Accounts. 1986 Gut: Stopped Random Walks. Limit Theorems and Applications. 1987 Guyon: Random Fields on a Network. Modeling, Statistics and Applications. 1995 Kallenberg: Foundations of Modern Probability. 1997, 2nd ed. 2002 Kallenberg: Probabilistic Symmetries and Invariance Principles.
    [Show full text]
  • Most Honourable Remembrance. the Life and Work of Thomas Bayes
    MOST HONOURABLE REMEMBRANCE. THE LIFE AND WORK OF THOMAS BAYES Andrew I. Dale Springer-Verlag, New York, 2003 668 pages with 29 illustrations The author of this book is professor of the Department of Mathematical Statistics at the University of Natal in South Africa. Andrew I. Dale is a world known expert in History of Mathematics, and he is also the author of the book “A History of Inverse Probability: From Thomas Bayes to Karl Pearson” (Springer-Verlag, 2nd. Ed., 1999). The book is very erudite and reflects the wide experience and knowledge of the author not only concerning the history of science but the social history of Europe during XVIII century. The book is appropriate for statisticians and mathematicians, and also for students with interest in the history of science. Chapters 4 to 8 contain the texts of the main works of Thomas Bayes. Each of these chapters has an introduction, the corresponding tract and commentaries. The main works of Thomas Bayes are the following: Chapter 4: Divine Benevolence, or an attempt to prove that the principal end of the Divine Providence and Government is the happiness of his creatures. Being an answer to a pamphlet, entitled, “Divine Rectitude; or, An Inquiry concerning the Moral Perfections of the Deity”. With a refutation of the notions therein advanced concerning Beauty and Order, the reason of punishment, and the necessity of a state of trial antecedent to perfect Happiness. This is a work of a theological kind published in 1731. The approaches developed in it are not far away from the rationalist thinking.
    [Show full text]
  • 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).
    [Show full text]
  • Cavendish the Experimental Life
    Cavendish The Experimental Life Revised Second Edition Max Planck Research Library for the History and Development of Knowledge Series Editors Ian T. Baldwin, Gerd Graßhoff, Jürgen Renn, Dagmar Schäfer, Robert Schlögl, Bernard F. Schutz Edition Open Access Development Team Lindy Divarci, Georg Pflanz, Klaus Thoden, Dirk Wintergrün. The Edition Open Access (EOA) platform was founded to bring together publi- cation initiatives seeking to disseminate the results of scholarly work in a format that combines traditional publications with the digital medium. It currently hosts the open-access publications of the “Max Planck Research Library for the History and Development of Knowledge” (MPRL) and “Edition Open Sources” (EOS). EOA is open to host other open access initiatives similar in conception and spirit, in accordance with the Berlin Declaration on Open Access to Knowledge in the sciences and humanities, which was launched by the Max Planck Society in 2003. By combining the advantages of traditional publications and the digital medium, the platform offers a new way of publishing research and of studying historical topics or current issues in relation to primary materials that are otherwise not easily available. The volumes are available both as printed books and as online open access publications. They are directed at scholars and students of various disciplines, and at a broader public interested in how science shapes our world. Cavendish The Experimental Life Revised Second Edition Christa Jungnickel and Russell McCormmach Studies 7 Studies 7 Communicated by Jed Z. Buchwald Editorial Team: Lindy Divarci, Georg Pflanz, Bendix Düker, Caroline Frank, Beatrice Hermann, Beatrice Hilke Image Processing: Digitization Group of the Max Planck Institute for the History of Science Cover Image: Chemical Laboratory.
    [Show full text]
  • Maty's Biography of Abraham De Moivre, Translated
    Statistical Science 2007, Vol. 22, No. 1, 109–136 DOI: 10.1214/088342306000000268 c Institute of Mathematical Statistics, 2007 Maty’s Biography of Abraham De Moivre, Translated, Annotated and Augmented David R. Bellhouse and Christian Genest Abstract. November 27, 2004, marked the 250th anniversary of the death of Abraham De Moivre, best known in statistical circles for his famous large-sample approximation to the binomial distribution, whose generalization is now referred to as the Central Limit Theorem. De Moivre was one of the great pioneers of classical probability the- ory. He also made seminal contributions in analytic geometry, complex analysis and the theory of annuities. The first biography of De Moivre, on which almost all subsequent ones have since relied, was written in French by Matthew Maty. It was published in 1755 in the Journal britannique. The authors provide here, for the first time, a complete translation into English of Maty’s biography of De Moivre. New mate- rial, much of it taken from modern sources, is given in footnotes, along with numerous annotations designed to provide additional clarity to Maty’s biography for contemporary readers. INTRODUCTION ´emigr´es that both of them are known to have fre- Matthew Maty (1718–1776) was born of Huguenot quented. In the weeks prior to De Moivre’s death, parentage in the city of Utrecht, in Holland. He stud- Maty began to interview him in order to write his ied medicine and philosophy at the University of biography. De Moivre died shortly after giving his Leiden before immigrating to England in 1740. Af- reminiscences up to the late 1680s and Maty com- ter a decade in London, he edited for six years the pleted the task using only his own knowledge of the Journal britannique, a French-language publication man and De Moivre’s published work.
    [Show full text]
  • From Abraham De Moivre to Johann Carl Friedrich Gauss
    International Journal of Engineering Science Invention (IJESI) ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726 www.ijesi.org ||Volume 7 Issue 6 Ver V || June 2018 || PP 28-34 A Brief Historical Overview Of the Gaussian Curve: From Abraham De Moivre to Johann Carl Friedrich Gauss Edel Alexandre Silva Pontes1 1Department of Mathematics, Federal Institute of Alagoas, Brazil Abstract : If there were only one law of probability to be known, this would be the Gaussian distribution. Faced with this uneasiness, this article intends to discuss about this distribution associated with its graph called the Gaussian curve. Due to the scarcity of texts in the area and the great demand of students and researchers for more information about this distribution, this article aimed to present a material on the history of the Gaussian curve and its relations. In the eighteenth and nineteenth centuries, there were several mathematicians who developed research on the curve, including Abraham de Moivre, Pierre Simon Laplace, Adrien-Marie Legendre, Francis Galton and Johann Carl Friedrich Gauss. Some researchers refer to the Gaussian curve as the "curve of nature itself" because of its versatility and inherent nature in almost everything we find. Virtually all probability distributions were somehow part or originated from the Gaussian distribution. We believe that the work described, the study of the Gaussian curve, its history and applications, is a valuable contribution to the students and researchers of the different areas of science, due to the lack of more detailed research on the subject. Keywords - History of Mathematics, Distribution of Probabilities, Gaussian Curve. ----------------------------------------------------------------------------------------------------------------------------- --------- Date of Submission: 09-06-2018 Date of acceptance: 25-06-2018 ----------------------------------------------------------------------------------------------------------------------------- ---------- I.
    [Show full text]
  • The Theory That Would Not Die Reviewed by Andrew I
    Book Review The Theory That Would Not Die Reviewed by Andrew I. Dale The solution, given more geometrico as Proposi- The Theory That Would Not Die: How Bayes’ tion 10 in [2], can be written today in a somewhat Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant compressed form as from Two Centuries of Controversy posterior probability ∝ likelihood × prior probability. Sharon Bertsch McGrayne Yale University Press, April 2011 Price himself added an appendix in which he used US$27.50, 336 pages the proposition in a prospective sense to find the ISBN-13: 978-03001-696-90 probability of the sun’s rising tomorrow given that it has arisen daily a million times. Later use In the early 1730sThomas Bayes (1701?–1761)was was made by Laplace, to whom one perhaps really appointed minister at the Presbyterian Meeting owes modern Bayesian methods. House on Mount Sion, Tunbridge Wells, a town that The degree to which one uses, or even supports, had developed around the restorative chalybeate Bayes’s Theorem (in some form or other) depends spring discovered there by Dudley, Lord North, in to a large extent on one’s views on the nature 1606. Apparently not one who was a particularly of probability. Setting this point aside, one finds popular preacher, Bayes would be recalled today, that the Theorem is generally used to update if at all, merely as one of the minor clergy of (to justify the updating of?) information in the eighteenth-century England, who also dabbled in light of new evidence as the latter is received, mathematics.
    [Show full text]
  • Abraham De Moivre R Cosθ + Isinθ N = Rn Cosnθ + Isinnθ R Cosθ + Isinθ = R
    Abraham de Moivre May 26, 1667 in Vitry-le-François, Champagne, France – November 27, 1754 in London, England) was a French mathematician famous for de Moivre's formula, which links complex numbers and trigonometry, and for his work on the normal distribution and probability theory. He was elected a Fellow of the Royal Society in 1697, and was a friend of Isaac Newton, Edmund Halley, and James Stirling. The social status of his family is unclear, but de Moivre's father, a surgeon, was able to send him to the Protestant academy at Sedan (1678-82). de Moivre studied logic at Saumur (1682-84), attended the Collège de Harcourt in Paris (1684), and studied privately with Jacques Ozanam (1684-85). It does not appear that De Moivre received a college degree. de Moivre was a Calvinist. He left France after the revocation of the Edict of Nantes (1685) and spent the remainder of his life in England. Throughout his life he remained poor. It is reported that he was a regular customer of Slaughter's Coffee House, St. Martin's Lane at Cranbourn Street, where he earned a little money from playing chess. He died in London and was buried at St Martin-in-the-Fields, although his body was later moved. De Moivre wrote a book on probability theory, entitled The Doctrine of Chances. It is said in all seriousness that De Moivre correctly predicted the day of his own death. Noting that he was sleeping 15 minutes longer each day, De Moivre surmised that he would die on the day he would sleep for 24 hours.
    [Show full text]
  • History of Probability (Part 4) - Inverse Probability and the Determination of Causes of Observed Events
    History of Probability (Part 4) - Inverse probability and the determination of causes of observed events. Thomas Bayes (c1702-1761) . By the early 1700s the work of Pascal, Fermat, and Huygens was well known, mainly couched in terms of odds and fair bets in gambling. Jacob Bernoulli and Abraham DeMoivre had made efforts to broaden the scope and interpretation of probability. Bernoulli had put probability measures on a scale between zero and one and DeMoivre had defined probability as a fraction of chances. But then there was a 50 year lull in further development in probability theory. This is surprising, but the “brains” of the day were busy applying the newly invented calculus to problems of physics – especially astronomy. At the beginning of the 18 th century a probability exercise like the following one was not too difficult. Suppose there are two boxes with white and black balls. Box A has 4 white and 1 black, box B has 2 white and 3 black. You pick one box at random, with probability 1/3 it will be Box A and 2/3 it will be B. Then you randomly pick one ball from the box. What is the probability you will end up with a white ball? We can model this scenario by a tree diagram. We also have the advantage of efficient symbolism. Expressions like = had not been developed by 1700. The idea of the probability of a specific event was just becoming useful in addition to the older emphasis on odds of one outcome versus another. Using the multiplication rule for independent events together with the addition rule, we find that the probability of picking a white ball is 8/15.
    [Show full text]
  • History of Statistics 2. Origin of the Normal Curve – Abraham Demoivre
    History of Statistics 2. Origin of the Normal Curve – Abraham DeMoivre (1667- 1754) The normal curve is perhaps the most important probability graph in all of statistics. Its formula is shown here with a familiar picture. The “ e” in the formula is the irrational number we use as the base for natural logarithms. μ and σ are the mean and standard deviation of the curve. The person who first derived the formula, Abraham DeMoivre (1667- 1754), was solving a gambling problem whose solution depended on finding the sum of the terms of a binomial distribution. Later work, especially by Gauss about 1800, used the normal distribution to describe the pattern of random measurement error in observational data. Neither man used the name “normal curve.” That expression did not appear until the 1870s. The normal curve formula appears in mathematics as a limiting case of what would happen if you had an infinite number of data points. To prove mathematically that some theoretical distribution is actually normal you have to be familiar with the idea of limits, with adding up more and more smaller and smaller terms. This process is a fundamental component of calculus. So it’s not surprising that the formula first appeared at the same time the basic ideas of calculus were being developed in England and Europe in the late 17 th and early 18 th centuries. The normal curve formula first appeared in a paper by DeMoivre in 1733. He lived in England, having left France when he was about 20 years old. Many French Protestants, the Huguenots, left France when the King canceled the Edict of Nantes which had given them civil rights.
    [Show full text]