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Fundamentals of Bayesian Epistemology 2

Arguments, Challenges, Alternatives

Michael G. Titelbaum

28 April 2022

ISBN: 9780192863157

416 pages
Paperback
234x156mm

Price: £25.00

Fundamentals of Bayesian Epistemology provides an accessible introduction to the key concepts and principles of the Bayesian formalism. Volume 2 introduces applications of Bayesianism to confirmation and decision theory, then gives a critical survey of arguments for and challenges to Bayesian epistemology.

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Description

Fundamentals of Bayesian Epistemology provides an accessible introduction to the key concepts and principles of the Bayesian formalism. Volume 2 introduces applications of Bayesianism to confirmation and decision theory, then gives a critical survey of arguments for and challenges to Bayesian epistemology.

  • Provides a guide to Bayesian methods, now widespread in many fields
  • Introduces Bayesian epistemology from the basics, with no background in the subject assumed
  • Motivates and presents the Bayesian formalism in multiple ways, to make it approachable and understandable
  • Further reading lists and thought-provoking exercises included in every chapter

About the Author(s)

Michael G. Titelbaum, Vilas Distinguished Achievement Professor in the Department of Philosophy, University of Wisconsin-Madison

Michael G. Titelbaum is a Vilas Distinguished Achievement Professor in the Department of Philosophy at the University of Wisconsin-Madison. After majoring in philosophy at Harvard, he had a brief career as a high school teacher. He then earned a PhD in philosophy from the University of California, Berkeley, and completed a Visiting Research Fellowship at the Australian National University. He began at UW-Madison in 2009, and was Chair of the Department of Philosophy 2019-2022.

Table of Contents

    III Applications
    6:Confirmation
    7:Decision Theory
    IV Arguments for Bayesianism
    8:Representation Theorems
    9:Dutch Book Arguments
    10:Accuracy Arguments
    Challenges and Objections
    11:Memory Loss and Self-Location
    12:Old Evidence, Logical Omniscience
    13:Alternatives to Bayesianism
    14:Comparisons, Ranges, Dempster-Shafer

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