Imprecise Priors as Expressions of Epistemic Values - a podcast by MCMP Team

Jim Joyce (Michigan) gives a talk at the Workshop on Imprecise Probabilities in Statistics and Philosophy (27-28 June, 2014) titled "Imprecise Priors as Expressions of Epistemic Values". Abstract: As is well known, imprecise prior probabilities can help us model beliefs in contexts where evidence is sparse, equivocal or vague. It is less well-known that they can also provide a useful way of representing certain kinds of indecision or uncertainty about epistemic values and inductive policies. If we use the apparatus of proper scoring rules to model a believer's epistemic values, then we can see her 'choice' of a prior as, partly, an articulation of her values. In contexts where epistemic values and inductive policies are less than fully definite, or where there is unresolved conflict among values, the imprecise prior will reject this indefiniteness in theoretically interesting ways.

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