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.
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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.
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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.
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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.