An Automated Prior Robustness Analysis in Bayesian Model Comparison

Normalized Power Prior Bayesian Analysis Normalized Power Prior Bayesian Analysis

Bayesian Model Robustness via Disparities

This paper develops a methodology for robust Bayesian inference through the use of disparities. Metrics such as Hellinger distance and negative exponential disparity have a long history in robust estimation in frequentist inference. We demonstrate that an equivalent robustification may be made in Bayesian inference by substituting an appropriately scaled disparity for the log likelihood to whic...

Robustness of Bayesian Pool-Based Active Learning Against Prior Misspecification

We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all α-approximate algorithms are robust (i.e., near α-approximate) if the utility is Lipschitz continuous in the...

Bayesian Wavelet Analysis with a Model Complexity Prior

SUMMARY In this paper Bayesian methods for the selection and shrinkage of wavelet coeecients are considered. The wavelet coeecients are assigned a zero mean normal prior with precision matrix. This allows for a more general model than has been previously studied and uncertainty in can be controlled through a hyperprior. It is argued that model interpretability is improved by foresaking the conj...

Prior-free Inference for Objective Bayesian Analysis and Model Selection

A new approach to Bayesian inference, named the prior-free inference, is introduced for developing objective Bayesian analysis based on information-theoretic approach. This new approach is essentially a Bayesian method but it does not depend on a prior distribution for unknown parameters. Thus, this approach not only has the advantages of the Bayesian approach but also can avoid the difficulty,...