Philosophy of Statistics

Series Editor:

  • Dov M. Gabbay, King's College London, UK
  • Paul Thagard, University of Waterloo, Canada
  • John Woods, University of British Columbia, Vancouver, Canada

Edited by

  • Prasanta S. Bandyopadhyay
  • Malcolm R. Forster, University of Wisconsin, Madison, USA

Statisticians and philosophers of science have many common interests but restricted communication with each other. This volume aims to remedy these shortcomings. It provides state-of-the-art research in the area of philosophy of statistics by encouraging numerous experts to communicate with one another without feeling “restricted” by their disciplines or thinking “piecemeal” in their treatment of issues.

A second goal of this book is to present work in the field without bias toward any particular statistical paradigm.

Broadly speaking, the essays in this Handbook are concerned with problems of induction, statistics and probability. For centuries, foundational problems like induction have been among philosophers’ favorite topics; recently, however, non-philosophers have increasingly taken a keen interest in these issues. This volume accordingly contains papers by both philosophers and non-philosophers, including scholars from nine academic disciplines.

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Scholars and graduate students interested in how philosophy takes scientific findings into account


Book information

  • Published: May 2011
  • Imprint: NORTH-HOLLAND
  • ISBN: 978-0-444-51862-0

Table of Contents

Philosophy of Statistics: An Introduction, by Prasanta S. Bandyopadhyay and Malcolm R. Forster
Part I. Probability & Statistics
Elementary Probability and Statistics: A Primer, by Prasanta S. Bandyopadhyay and Steve Cherry
Part II. Philosophical Controversies about Conditional Probability
Conditional Probability, by Alan Hájek
The Varieties of Conditional Probability, by Kenny Easwaran
Part III. Four Paradigms of Statistics
Classical Statistics
Paradigm Error Statistics, by Deborah G. Mayo and Aris Spanos
Significance Testing, by Michael Dickson and Davis Baird
Bayesian Paradigm
The Bayesian Decision-Theoretic Approach to Statistics, by Paul Weirich
Modern Bayesian Inference: Foundations and Objective Methods, by José M. Bernardo
Evidential Probability and Objective Bayesian Epistemology, by Gregory Wheeler and Jon Williamson
Confirmation Theory, by James Hawthorne
Challenges to Bayesian Confirmation Theory, by John D. Norton
Bayesianism as a Pure Logic of Inference, by Colin Howson
Bayesian Inductive Logic, Verisimilitude, and Statistics, by Roberto Festa
Likelihood Paradigm
Likelihood and its Evidential Framework, by Jeffrey D. Blume
Evidence, Evidence Functions, and Error Probabilities, by Mark L. Taper and Subhash R. Lele
Akaikean Paradigm
AIC Scores as Evidence - a Bayesian Interpretation, by Malcolm Forster and Elliott Sober
Part IV: The Likelihood Principle
The Likelihood Principle, by Jason Grossman
Part V: Recent Advances in Model Selection
AIC, BIC and Recent Advances in Model Selection, by Arijit Chakrabarti and Jayanta K. Ghosh
Posterior Model Probabilities, by A. Philip Dawid
Part VI: Attempts to Understand Different Aspects of “Randomness”
Defining Randomness, by Deborah Bennett
Mathematical Foundations of Randomness, by Abhijit Dasgupta
Part VII: Probabilistic and Statistical Paradoxes
Paradoxes of Probability, by Susan Vineberg
Statistical Paradoxes: Take It to The Limit, by C. Andy Tsao
Part VIII: Statistics and Inductive Inference
Statistics as Inductive Inference, by Jan-Willem Romeijn
Part IX: Various Issues about Causal Inference
Common Cause in Causal Inference, by Peter Spirtes
The Logic and Philosophy of Causal Inference: A Statistical Perspective, by Sander Greenland
Part X: Some Philosophical Issues Concerning Statistical Learning Theory
Statistical Learning Theory as a Framework for the Philosophy of Induction, by Gilbert Harman and Sanjeev Kulkarni
Testability and Statistical Learning Theory, by Daniel Steel
Part XI: Different Approaches to Simplicity Related to Inference and Truth
Luckiness and Regret in Minimum Description Length Inference, by Steven de Rooij and Peter D. GrĂĽnwald
MML, Hybrid Bayesian Network Graphical Models, Statistical, by Consistency, Invariance and Uniqueness, by
David L. Dowe
Simplicity, Truth and Probability, by Kevin T. Kelly
Part XII: Special Problems in Statistics/Computer Science
Normal Approximations, by Robert J. Boik
Stein’s Phenomenon, by Richard Charnigo and Cidambi Srinivasan
Data, Data, Everywhere: Statistical Issues in Data Mining, by Choh Man Teng
Part XIII: An Application of Statistics to Climate Change
An Application of Statistics in Climate Change: Detection of Nonlinear Changes in a Streamflow Timing Measure in the Columbia and Missouri Headwaters, by Mark C. Greenwood, Joel Harper and Johnnie Moore
Part XIV: Historical Approaches to Probability/Statistics
The Subjective and the Objective, by Sandy L. Zabell
Probability in Ancient India, by C. K. Raju