Philosophy of Statistics book cover

Philosophy of Statistics

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.

Audience

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

,

Published: May 2011

Imprint: North-holland

ISBN: 978-0-444-51862-0

Contents

  • Introduction
    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. Grünwald
    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

Advertisement

advert image