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This volume brings together a collection of essays on the history and philosophy of probability and statistics by one of the eminent scholars in these subjects. Written over the last fifteen years, they fall into three broad categories. The first deals with the use of symmetry arguments in inductive probability, in particular, their use in deriving rules of succession. The second group deals with three outstanding individuals who made lasting contributions to probability and statistics in very different ways: Frank Ramsey, R.A. Fisher, Alan Turing, and Abraham de Moivre. The last group of essays deals with the problem of "predicting the unpredictable."
The papers collected here are, with three exceptions, those presented at a conference on probability and causation held at the University of California at Irvine on July 15-19, 1985. The exceptions are that David Freedman and Abner Shimony were not able to contribute the papers that they presented to this volume, and that Clark Glymour who was not able to attend the conference did contribute a paper. We would like to thank the National Science Foundation and the School of Humanities of the University of California at Irvine for generous support. WILLIAM HARPER University of Western Ontario BRIAN SKYRMS University of California at Irvine VII INTRODUCTION TO CAUSATION, CHANCE, AND CREDENCE The search for causes is so central to science that it has sometimes been taken as the defining attribute of the scientific enterprise. Yet even after twenty-five centuries of philosophical analysis the meaning of "cause" is still a matter of controversy, among scientists as well as philosophers. Part of the problem is that the servicable concepts of causation built out of Necessity, Sufficiency, Locality, and Temporal Precedence were constructed for a deterministic world-view which has been obsolete since the advent of quantum theory. A physically credible theory of causation must be, at basis, statistical. And statistical analyses of caus ation may be of interest even when an underlying deterministic theory is assumed, as in classical statistical mechanics.
Brian Skyrms presents a set of influential essays which deploy formal methods to address epistemological and metaphysical questions. The first part of the book focuses on quantity; the second on degrees of belief, belief revision, and coherence; the third on aspects of inductive reasoning.
Inductive Logic is number ten in the 11-volume Handbook of the History of Logic. While there are many examples were a science split from philosophy and became autonomous (such as physics with Newton and biology with Darwin), and while there are, perhaps, topics that are of exclusively philosophical interest, inductive logic — as this handbook attests — is a research field where philosophers and scientists fruitfully and constructively interact. This handbook covers the rich history of scientific turning points in Inductive Logic, including probability theory and decision theory. Written by leading researchers in the field, both this volume and the Handbook as a whole are definitive reference tools for senior undergraduates, graduate students and researchers in the history of logic, the history of philosophy, and any discipline, such as mathematics, computer science, cognitive psychology, and artificial intelligence, for whom the historical background of his or her work is a salient consideration. - Chapter on the Port Royal contributions to probability theory and decision theory - Serves as a singular contribution to the intellectual history of the 20th century - Contains the latest scholarly discoveries and interpretative insights
Every age and every culture has relied on the incorporation of mathematics in their works of architecture to imbue the built environment with meaning and order. Mathematics is also central to the production of architecture, to its methods of measurement, fabrication and analysis. This two-volume edited collection presents a detailed portrait of the ways in which two seemingly different disciplines are interconnected. Over almost 100 chapters it illustrates and examines the relationship between architecture and mathematics. Contributors of these chapters come from a wide range of disciplines and backgrounds: architects, mathematicians, historians, theoreticians, scientists and educators. Through this work, architecture may be seen and understood in a new light, by professionals as well as non-professionals. Volume I covers architecture from antiquity through Egyptian, Mayan, Greek, Roman, Medieval, Inkan, Gothic and early Renaissance eras and styles. The themes that are covered range from symbolism and proportion to measurement and structural stability. From Europe to Africa, Asia and South America, the chapters span different countries, cultures and practices.
Probability is increasingly important for our understanding of the world. What is probability? How do we model it, and how do we use it? Timothy Childers presents a lively introduction to the foundations of probability and to philosophical issues it raises. He keeps technicalities to a minimum, and assumes no prior knowledge of the subject. He explains the main interpretations of probability-frequentist, propensity, classical, Bayesian, and objective Bayesian-and uses stimulating examples to bring the subject to life. All students of philosophy will benefit from an understanding of probability, and this is the book to provide it.
In Learning from Our Mistakes: Epistemology for the Real World, William J. Talbott provides a new framework for understanding the history of Western epistemology and uses it to propose a new way of understanding rational belief that can be applied to pressing social and political issues. Thisframework is used to articulate a new theory of prejudice and a new diagnosis of the sources of inequity in the U.S. criminal justice system, as well as insight into the proliferation of tribal and fascist epistemologies based on alt-facts and alt-truth.Talbott's new model of rational belief is not a model of a theorem prover in mathematics - It is a model of a good learner. Being a good learner requires sensitivity to clues, the imaginative ability to generate alternative explanatory narratives that fit the clues, and the ability to select themost coherent explanatory narrative. Sensitivity to clues requires sensitivity not only to evidence that supports one's own beliefs, but also to evidence that casts doubt on them. One of the most important characteristics of a good learner is the ability to correct mistakes.From this model, Talbott articulates nine principles that help to explain the difference between rational and irrational belief. Talbott contrasts his approach with the approach of historically important philosophers, including Socrates, Plato, Aristotle, Hume, Kant, Wittgenstein, and Kuhn, as wellas with a range of contemporary approaches, including pragmatism, Bayesianism, and naturalism. Learning from Our Mistakes offers a new lens through which to interpret the history of Western epistemology and analyze the complicated social and political phenomena facing us today.
According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard Jeffrey's 'radical probabilism'. Along the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sciences.
Statistical distributions are fundamental to Statistical Science and are a prime indispensable tool for its applications. This monograph is the first to examine an important but somewhat neglected field — univariate continuous distribution on a bounded domain, excluding the beta distribution. It provides an elementary but thorough discussion of “novel” contributions developed in recent years, such as the two-sided power, generalized trapezoidal and generalized Topp and Leone distributions, among others. It discusses a general framework for constructing two-sided distributions and some of its properties. It contains a comprehensive chapter on the triangular distribution as well as a chapter on earlier extensions not emphasized in existing literature. Special attention is given to estimation, in particular, non-standard maximum likelihood procedures. The applications are drawn mainly from the econometric and engineering domains.