A category-theoretic analysis represents conformal prediction as morphisms in stability and measurability categories, proves a commuting diagram decomposition into predictive distribution extraction and region derivation, and shows asymptotic compatibility with Bayesian predictive densities plus e-p
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Regularized e-processes add knowledge-based imprecise-probabilistic regularization to e-processes, yielding anytime-valid inference with efficiency gains and possibility-theoretic uncertainty quantification that satisfies the likelihood principle and avoids sure loss.
A review of possibilistic inferential models that deliver strong frequentist reliability and conditional imprecise-probabilistic reasoning, plus a generalization connecting them to bootstrap and conformal prediction methods.
citing papers explorer
-
A Category-Theoretic Analysis of Conformal Prediction
A category-theoretic analysis represents conformal prediction as morphisms in stability and measurability categories, proves a commuting diagram decomposition into predictive distribution extraction and region derivation, and shows asymptotic compatibility with Bayesian predictive densities plus e-p
-
Regularized e-processes: anytime valid inference with knowledge-based efficiency gains
Regularized e-processes add knowledge-based imprecise-probabilistic regularization to e-processes, yielding anytime-valid inference with efficiency gains and possibility-theoretic uncertainty quantification that satisfies the likelihood principle and avoids sure loss.
-
Possibilistic inferential models: a review
A review of possibilistic inferential models that deliver strong frequentist reliability and conditional imprecise-probabilistic reasoning, plus a generalization connecting them to bootstrap and conformal prediction methods.