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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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.