Bayes's Theorem

Gogoa 8 (1):138 (2008)
In introducing the papers of the symposiasts, I distinguish between statistical, physical, and evidential probability. The axioms of the probability calculus and so Bayes’s theorem can be expressed in terms of any of these kinds of probability. Sober questions the general utility of the theorem. Howson, Dawid, and Earman agree that it applies to the fields they discuss--statistics, assessment of guilt by juries, and miracles. Dawid and Earman consider that prior probabilities need to be supplied by empirical evidence, while Howson considers that there are no objective constraints on prior probabilities. I argue that simplicity is a crucial determinant of prior probability. Miller discussed how Bayes’s theorem can be interpreted so as to apply to physical probability
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ISBN(s) 0197262678  
DOI 10.1093/mind/113.451.591
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Prasanta S. Bandyopadhyay & Gordon Brittan (2010). Two Dogmas of Strong Objective Bayesianism. International Studies in the Philosophy of Science 24 (1):45 – 65.
G. Vision (2005). Truly Justified Belief. Synthese 146 (3):405 - 446.

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James Joyce, Bayes' Theorem. Stanford Encyclopedia of Philosophy.
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John Earman (2002). Bayes, Hume, Price, and Miracles. In Richard Swinburne (ed.), Bayes’s Theorem. Oxford University Press 91--110.
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