Inference, Method and Decision

Towards a Bayesian Philosophy of Science

  • Roger D. Rosenkrantz

Part of the Synthese Library book series (SYLI, volume 115)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Informative Inference

    1. Front Matter
      Pages 1-1
    2. Roger D. Rosenkrantz
      Pages 3-32
    3. Roger D. Rosenkrantz
      Pages 33-41
    4. Roger D. Rosenkrantz
      Pages 42-83
    5. Roger D. Rosenkrantz
      Pages 84-90
  3. Scientific Method

    1. Front Matter
      Pages 91-91
    2. Roger D. Rosenkrantz
      Pages 93-117
    3. Roger D. Rosenkrantz
      Pages 118-134
    4. Roger D. Rosenkrantz
      Pages 135-161
    5. Roger D. Rosenkrantz
      Pages 162-173
  4. Statistical Decision

    1. Front Matter
      Pages 175-175
    2. Roger D. Rosenkrantz
      Pages 177-187
    3. Roger D. Rosenkrantz
      Pages 188-223
    4. Roger D. Rosenkrantz
      Pages 224-241
    5. Roger D. Rosenkrantz
      Pages 242-256
  5. Back Matter
    Pages 257-270

About this book

Introduction

This book grew out of previously published papers of mine composed over a period of years; they have been reworked (sometimes beyond recognition) so as to form a reasonably coherent whole. Part One treats of informative inference. I argue (Chapter 2) that the traditional principle of induction in its clearest formulation (that laws are confirmed by their positive cases) is clearly false. Other formulations in terms of the 'uniformity of nature' or the 'resemblance of the future to the past' seem to me hopelessly unclear. From a Bayesian point of view, 'learning from experience' goes by conditionalization (Bayes' rule). The traditional stum­ bling block for Bayesians has been to fmd objective probability inputs to conditionalize upon. Subjective Bayesians allow any probability inputs that do not violate the usual axioms of probability. Many subjectivists grant that this liberality seems prodigal but own themselves unable to think of additional constraints that might plausibly be imposed. To be sure, if we could agree on the correct probabilistic representation of 'ignorance' (or absence of pertinent data), then all probabilities obtained by applying Bayes' rule to an 'informationless' prior would be objective. But familiar contra­ dictions, like the Bertrand paradox, are thought to vitiate all attempts to objectify 'ignorance'. BuUding on the earlier work of Sir Harold Jeffreys, E. T. Jaynes, and the more recent work ofG. E. P. Box and G. E. Tiao, I have elected to bite this bullet. In Chapter 3, I develop and defend an objectivist Bayesian approach.

Keywords

Karl R. Popper invariance philosophy of science probability

Authors and affiliations

  • Roger D. Rosenkrantz
    • 1
  1. 1.Virginia Polytechnic Institute and State UniversityBlacksburgUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-010-1237-9
  • Copyright Information Springer Science+Business Media B.V. 1977
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-90-277-0818-2
  • Online ISBN 978-94-010-1237-9
  • About this book