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.

Key Features

  • Provides a bridge between philosophy and current scientific findings
  • Covers theory and applications
  • Encourages multi-disciplinary dialogue


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

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<


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© 2011
North Holland
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