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Conformal Prediction Regions are Imprecise Highest Density Regions

2025/02/10 by Michele Caprio, Caprio, Michele, Yusuf Sale +3 · 3 citations
Computer Science · #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2502.06331

Abstract

Recently, Cella and Martin proved how, under an assumption called consonance, a credal set (i.e. a closed and convex set of probabilities) can be derived from the conformal transducer associated with transductive conformal prediction. We show that the Imprecise Highest Density Region (IHDR) associated with such a credal set corresponds to the classical Conformal Prediction Region. In proving this result, we establish a new relationship between Conformal Prediction and Imprecise Probability (IP) theories, via the IP concept of a cloud. A byproduct of our presentation is the discovery that consonant plausibility functions are monoid homomorphisms, a new algebraic property of an IP tool.

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