
Exact and efficient inference for Partial Bayes problems
Bayesian methods are useful for statistical inference. However, realwor...
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Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks
Bayesian neural networks (BNNs) hold great promise as a flexible and pri...
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Recasting GradientBased MetaLearning as Hierarchical Bayes
Metalearning allows an intelligent agent to leverage prior learning epi...
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Empirical Bayes methods for monitoring health care quality
The paper discusses empirical Bayes methodology for repeated quality com...
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SafeBayesian Generalized Linear Regression
We study generalized Bayesian inference under misspecification, i.e. whe...
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VIB is Half Bayes
In discriminative settings such as regression and classification there a...
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Bayesraking: Bayesian Finite Population Inference with Known Margins
Raking is widely used in categorical data modeling and survey practice b...
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Hierarchical Bayes Modeling for LargeScale Inference
Bayesian modeling is now ubiquitous in problems of largescale inference even when frequentist criteria are in mind for evaluating the performance of a procedure. By far most popular in literature of the past decade and a half are empirical Bayes methods, that have shown in practice to improve significantly over strictlyfrequentist competitors in many different problems. As an alternative to empirical Bayes methods, in this paper we propose hierarchical Bayes modeling for largescale problems, and address two separate points that, in our opinion, deserve more attention. The first is nonparametric "deconvolution" methods that are applicable also outside the sequence model. The second point is the adequacy of Bayesian modeling for situations where the parameters are by assumption deterministic. We provide partial answers to both: first, we demonstrate how our methodology applies in the analysis of a logistic regression model. Second, we appeal to Robbins's compound decision theory and provide an extension, to give formal justification for the Bayesian approach in the sequence case.
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