Subjective Probability the Real Thing
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Philosophy Reviewed
Unpublished Reviews Philosophy Reviewed Wednesday, July 11, 2018 Minds and Machines on Causality and the Brain June 2018, Volume 28, Issue 2, This volume of Minds and Machines is the product of a conference, which seems largely to have determined the contributions. Although purportedly about science, the essays are often principally directed at those philosophers of science who do not understand the banalities of the sciences they write about or are interested in. (Scientists tend to like this kind of stuff, because it is people saying what the scientists know or think. Everyone likes cheerleading.) Only one of the essays, Romeijin and Williamson's, makes any contribution a brain scientist could conceivably use. Romeijin and Williamson, Intervention and Identification in Latent Variable Modeling The authors actually do something. They show that if X, Y, L are binary, and L is the common cause of X, Y, and X, Y are measured and L is unmeasured and there and there are no other causal relations between X and Y, then an exogenous perturbation of the distribution of L allows identification of p(X | L) and p(Y | L) (and of course, p(X,Y | L) for all values of L, without knowledge of the distributions of L before and after perturbation except that the distributions are different. Of course, it isn't true if the relation between X, Y and L is linear, or if besides the common cause, X influences Y, or if L has more than two values, etc. The authors give no empirical example that realizes their result. -
21 on Probability and Cosmology: Inference Beyond Data? Martin Sahl´Ena
21 On Probability and Cosmology: Inference Beyond Data? Martin Sahl´ena Slightly expanded version of contribution to the book `The Philosophy of Cos- mology', Khalil Chamcham, Joseph Silk, John D. Barrow, Simon Saunders (eds.), Cambridge University Press, 2017. 21.1 Cosmological Model Inference with Finite Data In physical cosmology we are faced with an empirical context of gradually diminishing returns from new observations. This is true in a fundamental sense, since the amount of information we can expect to collect through as- tronomical observations is finite, owing to the fact that we occupy a particu- lar vantage point in the history and spatial extent of the Universe. Arguably, we may approach the observational limit in the foreseeable future, at least in relation to some scientific hypotheses (Ellis, 2014). There is no guaran- tee that the amount and types of information we are able to collect will be sufficient to statistically test all reasonable hypotheses that may be posed. There is under-determination both in principle and in practice (Zinkernagel, 2011; Butterfield, 2014; Ellis, 2014). These circumstances are not new, in- deed cosmology has had to contend with this problem throughout history. For example, Whitrow (1949) relates the same concerns, and points back to remarks by Blaise Pascal in the 17th century: \But if our view be arrested there let our imagination pass beyond; ... We may enlarge our conceptions beyond all imaginable space; we only produce atoms in comparison with the reality of things." Already with Thales, epistemological principles of unifor- mity and consistency have been used to structure the locally imaginable into something considered globally plausible. -
Rothamsted in the Making of Sir Ronald Fisher Scd FRS
Rothamsted in the Making of Sir Ronald Fisher ScD FRS John Aldrich University of Southampton RSS September 2019 1 Finish 1962 “the most famous statistician and mathematical biologist in the world” dies Start 1919 29 year-old Cambridge BA in maths with no prospects takes temporary job at Rothamsted Experimental Station In between 1919-33 at Rothamsted he makes a career for himself by creating a career 2 Rothamsted helped make Fisher by establishing and elevating the office of agricultural statistician—a position in which Fisher was unsurpassed by letting him do other things—including mathematical statistics, genetics and eugenics 3 Before Fisher … (9 slides) The problems were already there viz. o the relationship between harvest and weather o design of experimental trials and the analysis of their results Leading figures in agricultural science believed the problems could be treated statistically 4 Established in 1843 by Lawes and Gilbert to investigate the effective- ness of fertilisers Sir John Bennet Lawes Sir Joseph Henry Gilbert (1814-1900) land-owner, fertiliser (1817-1901) professional magnate and amateur scientist scientist (chemist) In 1902 tired Rothamsted gets make-over when Daniel Hall becomes Director— public money feeds growth 5 Crops and weather and experimental trials Crops & the weather o examined by agriculturalists—e.g. Lawes & Gilbert 1880 o subsequently treated more by meteorologists Experimental trials a hotter topic o treated in Journal of Agricultural Science o by leading figures Wood of Cambridge and Hall -
1978 Commencement Program, University Archives, University Of
UNIVERSITY of PENNSYLVANIA Two Hundred Thirtieth Commencement for the Conferring of Degrees FRANKLIN FIELD Monday, May 19, 1986 Contents University of Pennsylvania Page OFFICE OF THE SECRETARY The Commencement Ceremony 4 Commencement Notes 6 General Instructions for Commencement Day , 1911 Degrees in Course 8 The College of Arts and Sciences 8 The College of General Studies 16 Members of Graduating Glasses Will Please Read and Retain this Notice The School of Engineering and Applied Science 17 The Wharton School 25 The Wharton Evening School 29 For the Information of the Graduating Classes, the following Instructions are issued to The Wharton Graduate Division 31 Govern Their Actions on Commencement Day, Wednesday, June 21st The School of Nursing 36 The School of Medicine 38 All those who are to receive degrees at Commencement will assemble by Schools in HORTICULTURAL HALL (just south of the Academy of Music), not later than 10.15 a. m. The Law School 39 The Graduate School of Fine Arts 41 Full Academic Dress (i. e., cap, gown and hood) must be worn. The School of Dental Medicine 44 The Marshal in charge will start the march promptly at 10.45. Each class will be headed by its President and The School of Veterinary Medicine 45 Vice-President. Classes will move in columns of two in the following order: The Graduate School of Education 46 Classes of 1911 College and Graduate School. The School of Social Work 48 Class of 1911 Law. The Annenberg School of Communications 49 Class of 1911 Medical. The Graduate Faculties 49 Class of 1911 Dental. -
Counterfactual Desirability
Counterfactual Desirability Richard Bradley and H. Orri Stef´ansson Abstract The desirability of what actually occurs is often influenced by what could have been. Preferences based on such value dependencies between actual and counterfactual outcomes generate a class of problems for orthodox deci- sion theory, the best-known perhaps being the so-called Allais Paradox. In this paper we solve these problems by extending Richard Jeffrey’s decision theory to counterfactual prospects, using a multidimensional possible-world semantics for conditionals, and showing that preferences that are sensitive to counterfactual considerations can still be desirability maximising. We end the paper by investigating the conditions necessary and sufficient for a desirability function to be a standard expected utility function. It turns out that the additional conditions imply highly implausible epistemic principles. 1. Two Paradoxes of Rational Choice 2. Jeffrey Desirability 3. Counterfactuals 3.1 Probability and desirability of counterfactuals 3.2. Representations 4. Counterfactual-Dependent Preferences 1 4.1 Preference actualism and desirability maximisation 4.2 Modelling Allais' and Diamond's preferences 5. Ethical Actualism and Separability 5.1 Independence and Additive Separability 5.2 Ethical Actualism 5.3 Expected utility, Separability and Ethical Actualism 6. Concluding Remarks 7. Appendix: Definitions and Proofs 7.1 Jeffrey representations 7.2 Suppositional algebras 7.2.1 Probability conditions 7.2.2 Desirability-probability results 7.3 Characterisation results for expected utility 7.3.1 Necessity results 7.3.2 Sufficiency results The desirability of what actually occurs is often influenced by what could have been. Suppose you have been offered two jobs, one very exciting but with a sub- stantial risk of unemployment, the other less exciting but more secure. -
United States Attorney Carmen M. Ortiz District of Massachusetts
United States Attorney Carmen M. Ortiz District of Massachusetts FOR IMMEDIATE RELEASE CONTACT: CHRISTINA DiIORIO-STERLING September 30, 2015 Phone: (617) 748-3356 www.justice.gov/usao/ma/news.html [email protected] TEAMSTERS INDICTED FOR ATTEMPTED EXTORTION OF REALITY TELEVISION PRODUCTION COMPANY BOSTON – Five members of Teamsters Local 25 were arrested today in connection with attempting to extort a television production company that was filming a reality show in the Boston area in spring 2014. “The indictment alleges that a group of rogue Teamsters employed old school thug tactics to get no-work jobs from an out of town production company,” said United States Attorney Carmen M. Ortiz. “In the course of this alleged conspiracy, they managed to chase a legitimate business out of the City of Boston and then harassed the cast and crew when they set up shop in Milton. This kind of conduct reflects poorly on our city and must be addressed for what it is – not union organizing, but criminal extortion.” “While unions have the right to advocate on behalf of their members, they do not have the right to use violence and intimidation,” said Joseph R. Bonavolonta, Acting Special Agent in Charge of the Federal Bureau of Investigation, Boston Field Division. “The strong-arm tactics the FBI has seen in this case are egregious and our investigation is far from over. Today’s arrests should send a message to those who think they can get away with manipulating the system that they better think twice.” Mark Harrington, 61, of Andover; John Fidler, 51, of Holbrook; Daniel Redmond, 47, of Medford; Robert Cafarelli, 45, of Middleton; and Richard Jeffrey, 55, of Woburn, were indicted on conspiracy to extort and attempted extortion of a television production company in order to obtain no-work jobs for fellow Teamsters. -
Mathias Risse Curriculum Vitae
Mathias Risse Curriculum Vitae John F. Kennedy School of Government Office: (617) 495 9811 Harvard University Fax: (617) 495 4297 79 JFK St / Rubenstein 209 Cambridge, MA 02138 [email protected] USA https://www.hks.harvard.edu/about/faculty-staff-directory/mathias-risse Citizenship: German and American Employment Since 2018: Lucius N. Littauer Professor of Philosophy and Public Administration; Director of the Carr Center for Human Rights Policy; Affiliated Faculty in the Department of Philosophy 2000-2005: Assistant Professor, 2005 – 2010: Associate Professor, 2010-2018 Professor of Philosophy and Public Policy, John F. Kennedy School of Government, Harvard University 2000 - 2002: Assistant Professor of Philosophy, Department of Philosophy, Yale University Areas of Teaching and Research Areas of Specialization: Social and Political Philosophy, Ethics (Systematic, Applied) Areas of Competence: 19th Century German Philosophy, especially Nietzsche; Decision Theory (Individual and Group), Philosophy of Science (General); Logic Education 1995- 2000: Princeton University, Department of Philosophy Ph.D., Summer 2000; M.A., 1997 1990-1995: University of Bielefeld (Germany), Departments of Philosophy and Mathematics and Institute for Mathematical Economics M.S. (Diplom), 1996, Mathematics, supervisor Robert Aumann, Hebrew University; exam areas probability/measure theory, game theory, logic, algebraic topology; grade sehr gut (very good) B.S. (Vordiplom), 1992, Mathematics and Mathematical Economics, grade sehr gut B.A. (Zwischenprüfung), -
Basic Statistics
Statistics Kristof Reid Assistant Professor Medical University of South Carolina Core Curriculum V5 Financial Disclosures • None Core Curriculum V5 Further Disclosures • I am not a statistician • I do like making clinical decisions based on appropriately interpreted data Core Curriculum V5 Learning Objectives • Understand why knowing statistics is important • Understand the basic principles and statistical test • Understand common statistical errors in the medical literature Core Curriculum V5 Why should I care? Indications for statistics Core Curriculum V5 47% unable to determine study design 31% did not understand p values 38% did not understand sensitivity and specificity 83% could not use odds ratios Araoye et al, JBJS 2020;102(5):e19 Core Curriculum V5 50% (or more!) of clinical research publications have at least one statistical error Thiese et al, Journal of thoracic disease, 2016-08, Vol.8 (8), p.E726-E730 Core Curriculum V5 17% of conclusions not justified by results 39% of studies used the wrong analysis Parsons et al, J Bone Joint Surg Br. 2011;93-B(9):1154-1159. Core Curriculum V5 Are these two columns different? Column A Column B You have been asked by your insurance carrier 19 10 12 11 to prove that your total hip patient outcomes 10 9 20 19 are not statistically different than your competitor 10 17 14 10 next door. 21 19 24 13 Column A is the patient reported score for you 12 18 and column B for your competitor 17 8 12 17 17 10 9 9 How would you prove you are 24 10 21 9 18 18 different or better? 13 4 18 8 12 14 15 15 14 17 Core Curriculum V5 Are they still the same? Column A: Mean=15 Column B: Mean=12 SD=4 Core Curriculum V5 We want to know if we're making a difference Core Curriculum V5 What is statistics? a. -
Principles of the Design and Analysis of Experiments
A FRESH LOOK AT THE BASIC PRINCIPLES OF THE DESIGN AND ANALYSIS OF EXPERIMENTS F. YATES ROTHAMSTED EXPERIMENTAL STATION, HARPENDEN 1. Introduction When Professor Neyman invited me to attend the Fifth Berkeley Symposium, and give a paper on the basic principles of the design and analysis of experiments, I was a little hesitant. I felt certain that all those here must be thoroughly conversant with these basic principles, and that to mull over them again would be of little interest. This, however, is the first symposium to be held since Sir Ronald Fisher's death, and it does therefore seem apposite that a paper discussing some aspect of his work should be given. If so, what could be better than the design and analysis of experiments, which in its modern form he created? I do not propose today to give a history of the development of the subject. This I did in a paper presented in 1963 to the Seventh International Biometrics Congress [14]. Instead I want to take a fresh look at the logical principles Fisher laid down, and the action that flows from them; also briefly to consider certain modern trends, and see how far they are really of value. 2. General principles Fisher, in his first formal exposition of experimental design [4] laid down three basic principles: replication; randomization; local control. Replication and local control (for example, arrangement in blocks or rows and columns of a square) were not new, but the idea of assigning the treatments at random (subject to the restrictions imposed by the local control) was novel, and proved to be a most fruitful contribution. -
Turns in the Evolution of the Problem of Induction*
CARL G. HEMPEL TURNS IN THE EVOLUTION OF THE PROBLEM OF INDUCTION* 1. THE STANDARD CONCEPTION: INDUCTIVE "INFERENCE" Since the days of Hume's skeptical doubt, philosophical conceptions of the problem of induction and of ways in which it might be properly solved or dissolved have undergone a series of striking metamor- phoses. In my paper, I propose to examine some of those turnings, which seem to me to raise particularly important questions about the nature of empirical knowledge and especially scientific knowledge. Many, but by no means all, of the statements asserted by empirical science at a given time are accepted on the basis of previously established evidence sentences. Hume's skeptical doubt reflects the realization that most of those indirectly, or inferentially, accepted assertions rest on evidence that gives them no complete, no logically conclusive, support. This is, of course, the point of Hume's obser- vation that even if we have examined many occurrences of A and have found them all to be accompanied by B, it is quite conceivable, or logically possible, that some future occurrence of A might not be accompanied by B. Nor, we might add, does our evidence guarantee that past or present occurrences of A that we have not observed were- or are- accompanied by B, let alone that all occurrences ever of A are, without exception, accompanied by B. Yet, in our everyday pursuits as well as in scientific research we constantly rely on what I will call the method of inductive ac- ceptance, or MIA for short: we adopt beliefs, or expectations, about empirical matters on logically incomplete evidence, and we even base our actions on such beliefs- to the point of staking our lives on some of them. -
Notes: Hypothesis Testing, Fisher's Exact Test
Notes: Hypothesis Testing, Fisher’s Exact Test CS 3130 / ECE 3530: Probability and Statistics for Engineers Novermber 25, 2014 The Lady Tasting Tea Many of the modern principles used today for designing experiments and testing hypotheses were intro- duced by Ronald A. Fisher in his 1935 book The Design of Experiments. As the story goes, he came up with these ideas at a party where a woman claimed to be able to tell if a tea was prepared with milk added to the cup first or with milk added after the tea was poured. Fisher designed an experiment where the lady was presented with 8 cups of tea, 4 with milk first, 4 with tea first, in random order. She then tasted each cup and reported which four she thought had milk added first. Now the question Fisher asked is, “how do we test whether she really is skilled at this or if she’s just guessing?” To do this, Fisher introduced the idea of a null hypothesis, which can be thought of as a “default position” or “the status quo” where nothing very interesting is happening. In the lady tasting tea experiment, the null hypothesis was that the lady could not really tell the difference between teas, and she is just guessing. Now, the idea of hypothesis testing is to attempt to disprove or reject the null hypothesis, or more accurately, to see how much the data collected in the experiment provides evidence that the null hypothesis is false. The idea is to assume the null hypothesis is true, i.e., that the lady is just guessing. -
Interpretations of Probability First Published Mon Oct 21, 2002; Substantive Revision Mon Dec 19, 2011
Open access to the Encyclopedia has been made possible, in part, with a financial contribution from the Australian National University Library. We gratefully acknowledge this support. Interpretations of Probability First published Mon Oct 21, 2002; substantive revision Mon Dec 19, 2011 ‘Interpreting probability’ is a commonly used but misleading characterization of a worthy enterprise. The so-called ‘interpretations of probability’ would be better called ‘analyses of various concepts of probability’, and ‘interpreting probability’ is the task of providing such analyses. Or perhaps better still, if our goal is to transform inexact concepts of probability familiar to ordinary folk into exact ones suitable for philosophical and scientific theorizing, then the task may be one of ‘explication’ in the sense of Carnap (1950). Normally, we speak of interpreting a formal system , that is, attaching familiar meanings to the primitive terms in its axioms and theorems, usually with an eye to turning them into true statements about some subject of interest. However, there is no single formal system that is ‘probability’, but rather a host of such systems. To be sure, Kolmogorov's axiomatization, which we will present shortly, has achieved the status of orthodoxy, and it is typically what philosophers have in mind when they think of ‘probability theory’. Nevertheless, several of the leading ‘interpretations of probability’ fail to satisfy all of Kolmogorov's axioms, yet they have not lost their title for that. Moreover, various other quantities that have nothing to do with probability do satisfy Kolmogorov's axioms, and thus are interpretations of it in a strict sense: normalized mass, length, area, volume, and other quantities that fall under the scope of measure theory, the abstract mathematical theory that generalizes such quantities.