A practical strategy for valid partial prior-dependent possibilistic inference

This paper considers statistical inference in contexts where only incomplete prior information is available. We develop a practical construction of a suitably valid inferential model (IM) that (a) takes the form of a possibility measure, and (b) depends mainly on the likelihood and partial prior. We also propose a general computational algorithm through which the proposed IM can be evaluated in applications.