Abstract
This paper studies the theoretical properties of Bayesian predictions and shows that under minimal conditions we can derive finite sample bounds for the loss incurred using Bayesian predictions under the Kullback-Leibler divergence. In particular, the concept of universality of predictions is discussed and universality is established for Bayesian predictions in a variety of settings. These include predictions under almost arbitrary loss functions, model averaging, predictions in a non-stationary environment and under model misspecification.
Citation
Alessio Sancetta. "Universality of Bayesian Predictions." Bayesian Anal. 7 (1) 1 - 36, March 2012. https://doi.org/10.1214/12-BA701
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