- Bayesian Anal.
- Volume 7, Number 1 (2012), 1-36.
Universality of Bayesian Predictions
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.
Bayesian Anal., Volume 7, Number 1 (2012), 1-36.
First available in Project Euclid: 13 June 2012
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Sancetta, Alessio. Universality of Bayesian Predictions. Bayesian Anal. 7 (2012), no. 1, 1--36. doi:10.1214/12-BA701. https://projecteuclid.org/euclid.ba/1339616721
- Related item: Bertrand Clarke. Comment on Article by Sancetta. Bayesian Anal., Vol. 7, Iss. 1 (2012), 37-44.
- Related item: Feng Liang. Comment on Article by Sancetta. Bayesian Anal., Vol. 7, Iss. 1 (2012), 45-46.
- Related item: Alessio Sancetta. Rejoinder. Bayesian Anal., Vol. 7, Iss. 1 (2012), 47-50.