David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
OUP Oxford (2010)
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 outcomes Objective 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 to learn from experience. It has also been accused of being computationally intractable, susceptible to paradox, language dependent, and of not being objective enough. Especially suitable for graduates or researchers in philosophy of science, foundations of statistics and artificial intelligence, the book argues that these criticisms can be met and that objective Bayesianism is a promising theory with an exciting agenda for further research.
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Jon Williamson (2013). Why Frequentists and Bayesians Need Each Other. Erkenntnis 78 (2):293-318.
Matthias Unterhuber & Gerhard Schurz (2013). The New Tweety Puzzle: Arguments Against Monistic Bayesian Approaches in Epistemology and Cognitive Science. Synthese 190 (8):1407-1435.
Darrell P. Rowbottom (2013). Empirical Evidence Claims Are a Priori. Synthese 190 (14):2821-2834.
Darrell P. Rowbottom (2012). Identification in Games: Changing Places. Erkenntnis 77 (2):197-206.
Darrell P. Rowbottom (2013). Group Level Interpretations of Probability: New Directions. Pacific Philosophical Quarterly 94 (2):188-203.
Similar books and articles
Jon Williamson (2011). Objective Bayesianism, Bayesian Conditionalisation and Voluntarism. Synthese 178 (1):67-85.
Jon Williamson, Motivating Objective Bayesianism: From Empirical Constraints to Objective Probabilities.
Darrell P. Rowbottom (2012). Objective Bayesianism Defended? Metascience 21 (1):193-196.
J. Williamson (2012). Calibration and Convexity: Response to Gregory Wheeler. British Journal for the Philosophy of Science 63 (4):851-857.
Prasanta S. Bandyopadhyay & Gordon Brittan (2010). Two Dogmas of Strong Objective Bayesianism. International Studies in the Philosophy of Science 24 (1):45 – 65.
J. Williamson (2006). From Bayesianism to the Epistemic View of Mathematics: Review of R. Jeffrey, Subjective Probability: The Real Thing. [REVIEW] Philosophia Mathematica 14 (3):365-369.
Jon Williamson (2008). Objective Bayesianism with Predicate Languages. Synthese 163 (3):341 - 356.
Jon Williamson (2007). Inductive Influence. British Journal for the Philosophy of Science 58 (4):689 - 708.
Kenny Easwaran (2011). Bayesianism I: Introduction and Arguments in Favor. Philosophy Compass 6 (5):312-320.
Richard Bradley (2001). Ramsey and the Measurement of Belief. In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism.
Alan Hájek & Stephan Hartmann (2010). Bayesian Epistemology. In J. Dancy et al (ed.), A Companion to Epistemology. Blackwell.
Added to index2012-01-31
Total downloads2 ( #345,621 of 1,099,048 )
Recent downloads (6 months)2 ( #175,277 of 1,099,048 )
How can I increase my downloads?