Description

Book Synopsis

How strongly should you believe the various propositions that you can express?That is the key question facing Bayesian epistemology. Subjective Bayesians hold that it is largely (though not entirely) up to the agent as to which degrees of belief to adopt. Objective Bayesians, on the other hand, maintain that appropriate degrees of belief are largely (though not entirely) determined by the agent''s evidence. This book states and defends a version of objective Bayesian epistemology. According to this version, objective Bayesianism is characterized by three norms: Probability - degrees of belief should be probabilities Calibration - they should be calibrated with evidence Equivocation - they should otherwise equivocate between basic outcomesObjective Bayesianism has been challenged on a number of different fronts. For example, some claim it is poorly motivated, or fails to handle qualitative evidence, or yields counter-intuitive degrees of belief after updating, or suffers from a failure

Table of Contents

Preface ; 1. Introduction ; 2. Objective Bayesianism ; 3. Motivation ; 4. Updating ; 5. Predicate Languages ; 6. Objective Bayesian Nets ; 7. Probabilistic Logic ; 8. Judgement Aggregation ; 9. Languages and Relativity ; 10. Objective Bayesianism in Perspective ; References ; Index

In Defence of Objective Bayesianism

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    A Hardback by Jon Williamson

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      Book details

      Published 13 May 2010
      ISBN-13 9780199228003
      978-0199228003
      ISBN-10 0199228000

      Description

      Book Synopsis

      How strongly should you believe the various propositions that you can express?That is the key question facing Bayesian epistemology. Subjective Bayesians hold that it is largely (though not entirely) up to the agent as to which degrees of belief to adopt. Objective Bayesians, on the other hand, maintain that appropriate degrees of belief are largely (though not entirely) determined by the agent''s evidence. This book states and defends a version of objective Bayesian epistemology. According to this version, objective Bayesianism is characterized by three norms: Probability - degrees of belief should be probabilities Calibration - they should be calibrated with evidence Equivocation - they should otherwise equivocate between basic outcomesObjective Bayesianism has been challenged on a number of different fronts. For example, some claim it is poorly motivated, or fails to handle qualitative evidence, or yields counter-intuitive degrees of belief after updating, or suffers from a failure

      Table of Contents

      Preface ; 1. Introduction ; 2. Objective Bayesianism ; 3. Motivation ; 4. Updating ; 5. Predicate Languages ; 6. Objective Bayesian Nets ; 7. Probabilistic Logic ; 8. Judgement Aggregation ; 9. Languages and Relativity ; 10. Objective Bayesianism in Perspective ; References ; Index

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