On Bayesian learning via loss functions |
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Authors: | Pier Giovanni Bissiri Stephen G. Walker |
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Affiliation: | 1. Dipartimento di Statistica, Università degli Studi di Milano-Bicocca, Edificio U7, via Bicocca degli Arcimboldi 8, 20126 Milano, Italy;2. SMSAS, University of Kent, Canterbury, Kent CT2 7NZ, UK |
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Abstract: | We provide a decision theoretic approach to the construction of a learning process in the presence of independent and identically distributed observations. Starting with a probability measure representing beliefs about a key parameter, the approach allows the measure to be updated via the solution to a well defined decision problem. While the learning process encompasses the Bayesian approach, a necessary asymptotic consideration then actually implies the Bayesian learning process is best. This conclusion is due to the requirement of posterior consistency for all models and of having standardized losses between probability distributions. This is shown considering a specific continuous model and a very general class of discrete models. |
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Keywords: | Bayesian inference Posterior distribution Loss function Kullback&ndash Leibler divergence g-Divergence |
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