Abstract
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.
D. Hose—Partially supported by the Deutsche Forschungsgemeinschaft (DFG) project no. 319924547 (HA2798/9-2).
M. Hanss—Partially supported by the U.S. National Science Foundation, SES–205122.
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Hose, D., Hanss, M., Martin, R. (2022). A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science(), vol 13506. Springer, Cham. https://doi.org/10.1007/978-3-031-17801-6_19
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DOI: https://doi.org/10.1007/978-3-031-17801-6_19
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