Bayesian Inference under Ambiguity: Conditional Prior Belief Functions
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Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, PMLR 62:7384, 2017.
Abstract
Bayesian inference under imprecise prior information is studied: the starting point is a precise strategy $σ$ and a full Bconditional prior belief function $Bel_B$, conveying ambiguity in probabilistic prior information. In finite spaces, we give a closed form expression for the lower envelope $\underline{P}$ of the class of full conditional probabilities dominating $(Bel_B,σ)$ and, in particular, for the related “posterior probabilities”. The assessment $(Bel_B,σ)$ is a coherent lower conditional probability in the sense of Williams and the characterized lower envelope $\underline{P}$ coincides with its natural extension.
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