Statistics Seminars: Objective Bayesianism and learning from experience
12 May 2006 00:00 in CM107
"Objective Bayesianism has been criticised for not allowing learning from experience: it is claimed that an agent must give degree of belief 1/2 to the next raven being black, however many other black ravens have been observed. I argue that this objection can be overcome by appealing to *objective Bayesian nets*, a formalism for representing objective Bayesian degrees of belief. Under this account, previous observations exert an *inductive influence* on the next observation. I show how this approach can be used to capture the Johnson-Carnap continuum of inductive methods, as well as the Nix-Paris continuum, and show how inductive influence can be measured."
CM107 @ 1:30
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