Was Bayes a Bayesian?
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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. -
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. -
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. -
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. -
Probabilities, Random Variables and Distributions A
Probabilities, Random Variables and Distributions A Contents A.1 EventsandProbabilities................................ 318 A.1.1 Conditional Probabilities and Independence . ............. 318 A.1.2 Bayes’Theorem............................... 319 A.2 Random Variables . ................................. 319 A.2.1 Discrete Random Variables ......................... 319 A.2.2 Continuous Random Variables ....................... 320 A.2.3 TheChangeofVariablesFormula...................... 321 A.2.4 MultivariateNormalDistributions..................... 323 A.3 Expectation,VarianceandCovariance........................ 324 A.3.1 Expectation................................. 324 A.3.2 Variance................................... 325 A.3.3 Moments................................... 325 A.3.4 Conditional Expectation and Variance ................... 325 A.3.5 Covariance.................................. 326 A.3.6 Correlation.................................. 327 A.3.7 Jensen’sInequality............................. 328 A.3.8 Kullback–LeiblerDiscrepancyandInformationInequality......... 329 A.4 Convergence of Random Variables . 329 A.4.1 Modes of Convergence . 329 A.4.2 Continuous Mapping and Slutsky’s Theorem . 330 A.4.3 LawofLargeNumbers........................... 330 A.4.4 CentralLimitTheorem........................... 331 A.4.5 DeltaMethod................................ 331 A.5 ProbabilityDistributions............................... 332 A.5.1 UnivariateDiscreteDistributions...................... 333 A.5.2 Univariate Continuous Distributions . 335 -
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. -
Binomial and Multinomial Distributions
Binomial and multinomial distributions Kevin P. Murphy Last updated October 24, 2006 * Denotes more advanced sections 1 Introduction In this chapter, we study probability distributions that are suitable for modelling discrete data, like letters and words. This will be useful later when we consider such tasks as classifying and clustering documents, recognizing and segmenting languages and DNA sequences, data compression, etc. Formally, we will be concerned with density models for X ∈{1,...,K}, where K is the number of possible values for X; we can think of this as the number of symbols/ letters in our alphabet/ language. We assume that K is known, and that the values of X are unordered: this is called categorical data, as opposed to ordinal data, in which the discrete states can be ranked (e.g., low, medium and high). If K = 2 (e.g., X represents heads or tails), we will use a binomial distribution. If K > 2, we will use a multinomial distribution. 2 Bernoullis and Binomials Let X ∈{0, 1} be a binary random variable (e.g., a coin toss). Suppose p(X =1)= θ. Then − p(X|θ) = Be(X|θ)= θX (1 − θ)1 X (1) is called a Bernoulli distribution. It is easy to show that E[X] = p(X =1)= θ (2) Var [X] = θ(1 − θ) (3) The likelihood for a sequence D = (x1,...,xN ) of coin tosses is N − p(D|θ)= θxn (1 − θ)1 xn = θN1 (1 − θ)N0 (4) n=1 Y N N where N1 = n=1 xn is the number of heads (X = 1) and N0 = n=1(1 − xn)= N − N1 is the number of tails (X = 0). -
Arxiv:1808.10173V2 [Stat.AP] 30 Aug 2020
AN INTRODUCTION TO INDUCTIVE STATISTICAL INFERENCE FROM PARAMETER ESTIMATION TO DECISION-MAKING Lecture notes for a quantitative–methodological module at the Master degree (M.Sc.) level HENK VAN ELST August 30, 2020 parcIT GmbH Erftstraße 15 50672 Köln Germany ORCID iD: 0000-0003-3331-9547 E–Mail: [email protected] arXiv:1808.10173v2 [stat.AP] 30 Aug 2020 E–Print: arXiv:1808.10173v2 [stat.AP] © 2016–2020 Henk van Elst Dedicated to the good people at Karlshochschule Abstract These lecture notes aim at a post-Bachelor audience with a background at an introductory level in Applied Mathematics and Applied Statistics. They discuss the logic and methodology of the Bayes–Laplace ap- proach to inductive statistical inference that places common sense and the guiding lines of the scientific method at the heart of systematic analyses of quantitative–empirical data. Following an exposition of ex- actly solvable cases of single- and two-parameter estimation problems, the main focus is laid on Markov Chain Monte Carlo (MCMC) simulations on the basis of Hamiltonian Monte Carlo sampling of poste- rior joint probability distributions for regression parameters occurring in generalised linear models for a univariate outcome variable. The modelling of fixed effects as well as of correlated varying effects via multi-level models in non-centred parametrisation is considered. The simulation of posterior predictive distributions is outlined. The assessment of a model’s relative out-of-sample posterior predictive accuracy with information entropy-based criteria WAIC and LOOIC and model comparison with Bayes factors are addressed. Concluding, a conceptual link to the behavioural subjective expected utility representation of a single decision-maker’s choice behaviour in static one-shot decision problems is established. -
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. -
Bayes Rule in Perception, Action and Cognition
Bayes rule in perception, action and cognition Daniel M. Wolpert and Zoubin Ghahramani Department of Engineering, University of Cambridge, Trumpington Street, Cambridge Cognition and intelligent behaviour are fundamentally tied to the ability to survive in an uncertain and changing environment. Our only access to knowledge about the world is through our senses which provide information that is usually corrupted by random fluctuations, termed noise, and may provide ambiguous information about the possible states of the environment. Moreover, when we act on the world through our motor system, the commands we send to our muscles are also corrupted by variability or noise. This combined sensory and motor variability limits the precision with which we can perceive and act on the world. Here we will review the framework of Bayesian decision theory, which has emerged as a principled approach to handle uncertain information in an attempt to behave optimally, and how this framework can be used to understand sensory, motor and cognitive processes. Bayesian Theory is named after Thomas Bayes (Figure 1) who was an 18th century English Presbyterian minister. He is only known to have published two works during his life, of which only one dealt with mathematics in which he defended the logical foundation of Isaac Newton’s methods against contemporary criticism. After Bayes death, his friend Richard Price found an interesting mathematical proof among Bayes’ papers and sent the paper to the Editor of the Philosophical Transactions of the Royal Society stating “I now send you an essay which I have found among the papers of our deceased friend Mr Bayes, and which, in my opinion, has great merit....” The paper was subsequently published in 1764 as “Essay towards solving a problem in the doctrine of chances.” In the latter half of the 20th century Bayesian approaches have become a mainstay of statistics and a more general framework has now emerged, termed Bayesian decision theory (BDT). -
Bayesian Statistics: Thomas Bayes to David Blackwell
Bayesian Statistics: Thomas Bayes to David Blackwell Kathryn Chaloner Department of Biostatistics Department of Statistics & Actuarial Science University of Iowa [email protected], or [email protected] Field of Dreams, Arizona State University, Phoenix AZ November 2013 Probability 1 What is the probability of \heads" on a toss of a fair coin? 2 What is the probability of \six" upermost on a roll of a fair die? 3 What is the probability that the 100th digit after the decimal point, of the decimal expression of π equals 3? 4 What is the probability that Rome, Italy, is North of Washington DC USA? 5 What is the probability that the sun rises tomorrow? (Laplace) 1 1 1 My answers: (1) 2 (2) 6 (3) 10 (4) 0.99 Laplace's answer to (5) 0:9999995 Interpretations of Probability There are several interpretations of probability. The interpretation leads to methods for inferences under uncertainty. Here are the 2 most common interpretations: 1 as a long run frequency (often the only interpretation in an introductory statistics course) 2 as a subjective degree of belief You cannot put a long run frequency on an event that cannot be repeated. 1 The 100th digit of π is or is not 3. The 100th digit is constant no matter how often you calculate it. 2 Similarly, Rome is North or South of Washington DC. The Mathematical Concept of Probability First the Sample Space Probability theory is derived from a set of rules and definitions. Define a sample space S, and A a set of subsets of S (events) with specific properties. -
On Non-Informativeness in a Classical Bayesian Inference Problem
On non-informativeness in a classical Bayesian inference problem Citation for published version (APA): Coolen, F. P. A. (1992). On non-informativeness in a classical Bayesian inference problem. (Memorandum COSOR; Vol. 9235). Technische Universiteit Eindhoven. Document status and date: Published: 01/01/1992 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.