Cover Image

Fundamentals of Bayesian epistemology. 1, Introducing credences /

Show other versions (1)

"This book introduces readers to the fundamentals of Bayesian epistemology. It begins by motivating and explaining the idea of a degree of belief (also known as a "credence"). It then presents Bayesians' five core normative rules governing degrees of belief: Kolmogorov's thr...

Full description

Bibliographic Details
Main Author: Titelbaum, Michael G. (Author)
Format: eBook
Language:English
Published: Oxford : Oxford University Press, 2022.
Edition:First edition.
Subjects:
Bayesian statistical decision theory.
Knowledge, Theory of.
Belief and doubt.
Probabilities.
Théorie de la décision bayésienne.
Théorie de la connaissance.
Croyance et doute.
Probabilités.
epistemology.
probability.
Bayesian statistical decision theory
Belief and doubt
Knowledge, Theory of
Probabilities
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Cover
  • Fundamentals of Bayesian Epistemology 1: Introducing Credences
  • Copyright
  • Dedication
  • Contents
  • Quick Reference
  • Preface
  • 0.1 What's in this book
  • 0.2 How to read-and teach-this book
  • 0.3 Acknowledgments
  • Notes
  • PART I: OUR SUBJECT
  • 1: Beliefs and Degrees of Belief
  • 1.1 Binary beliefs
  • 1.1.1 Classificatory, comparative, quantitative
  • 1.1.2 Shortcomings of binary belief
  • 1.2 From binary to graded
  • 1.2.1 Comparative confidence
  • 1.2.2 Bayesian epistemology
  • 1.2.3 Relating beliefs and credences
  • 1.3 The rest of this book
  • 1.5 Further reading
  • Introductions and Overviews
  • Classic Texts
  • Extended Discussion
  • Notes
  • PART II: THE BAYESIAN FORMALISM
  • 2: Probability Distributions
  • 2.1 Propositions and propositional logic
  • 2.1.1 Relations among propositions
  • 2.1.2 State-descriptions
  • 2.1.3 Predicate logic
  • 2.2 The probability axioms
  • 2.2.1 Consequences of the probability axioms
  • 2.2.2 A Bayesian approach to the Lottery scenario
  • 2.2.3 Doxastic possibilities
  • 2.2.4 Probabilities are weird! The Conjunction Fallacy
  • 2.3 Alternative representations of probability
  • 2.3.1 Probabilities in Venn diagrams
  • 2.3.2 Probability tables
  • 2.3.3 Using probability tables
  • 2.4 What the probability calculus adds
  • 2.5 Exercises
  • 2.6 Further reading
  • Introductions and Overviews
  • Classic Texts
  • Extended Discussion
  • Notes
  • 3: Conditional Credences
  • 3.1 Conditional credences and the Ratio Formula
  • 3.1.1 The Ratio Formula
  • 3.1.2 Consequences of the Ratio Formula
  • 3.1.3 Bayes's Theorem
  • 3.2 Relevance and independence
  • 3.2.1 Conditional independence and screening off
  • 3.2.2 The Gambler's Fallacy
  • 3.2.3 Probabilities are weird! Simpson's Paradox
  • 3.2.4 Correlation and causation
  • 3.3 Conditional credences and conditionals
  • 3.4 Exercises
  • 3.5 Further reading
  • Introductions and Overviews
  • Classic Texts
  • Extended Discussion
  • Notes
  • 4: Updating by Conditionalization
  • 4.1 Conditionalization
  • 4.1.1 Consequences of Conditionalization
  • 4.1.2 Probabilities are weird! The Base Rate Fallacy
  • 4.2 Evidence and certainty
  • 4.2.1 Probabilities are weird! Total Evidence and the Monty Hall Problem
  • 4.3 Priors and standards
  • 4.3.1 Initial priors
  • 4.3.2 Epistemic standards
  • 4.3.3 Hypothetical priors
  • 4.4 Exercises
  • 4.5 Further reading
  • Introductions and Overviews
  • Classic Texts
  • Extended Discussion
  • Notes
  • 5: Further Rational Constraints
  • 5.1 Subjective and Objective Bayesianism
  • 5.1.1 Frequencies and propensities
  • 5.1.2 Two distinctions in Bayesianism
  • 5.2 Deference principles
  • 5.2.1 The Principal Principle
  • 5.2.2 Expert principles and Reflection
  • 5.3 The Principle of Indifference
  • 5.4 Credences for infinitely many possibilities
  • 5.5 Jeffrey Conditionalization
  • 5.6 Exercises
  • 5.7 Further reading
  • Subjective and Objective Bayesianism
  • Deference Principles
  • The Principle of Indifference