The paper proves sharp O(ε² log(1/ε)/log log(1/ε)) regret bounds for unregularized Bayes rules with compactly supported priors via polynomial approximation, improving on prior regularized results with extra log factors.
Handbook of Bayesian, Fiducial, and Frequentist Inference , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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A generalized Tweedie identity and moment-generating-function representation enable nonparametric recovery of full posteriors for heteroscedastic normal means with unknown variances without specifying a prior.
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Sharp regret-Hellinger bounds for Gaussian empirical Bayes via polynomial approximation
The paper proves sharp O(ε² log(1/ε)/log log(1/ε)) regret bounds for unregularized Bayes rules with compactly supported priors via polynomial approximation, improving on prior regularized results with extra log factors.
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Nonparametric f-Modeling for Empirical Bayes Inference with Unequal and Unknown Variances
A generalized Tweedie identity and moment-generating-function representation enable nonparametric recovery of full posteriors for heteroscedastic normal means with unknown variances without specifying a prior.