# Statistical Inference for Bayesian Risk Minimization via Exponentially Tilted Empirical Likelihood

@inproceedings{Tang2021StatisticalIF, title={Statistical Inference for Bayesian Risk Minimization via Exponentially Tilted Empirical Likelihood}, author={Rong Tang and Yun Yang}, year={2021} }

The celebrated Bernstein von-Mises theorem ensures that credible regions from Bayesian posterior are well-calibrated when the model is correctly-specified, in the frequentist sense that their coverage probabilities tend to the nominal values as data accrue. However, this conventional Bayesian framework is known to lack robustness when the model is misspecified or only partly specified, such as in quantile regression, risk minimization based supervised/unsupervised learning and robust estimation… Expand

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