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- W1640105997 abstract "Several topics concerning noninformative priors and prediction for group models are considered. The Kullback‐Leibler divergence from the true distribution to a predictive distribution is adopted as a loss function.When a model has a group structure, the right invariant measure is often recommended as a noninformative prior. We consider group models and show that the predictive distribution based on the right invariant measure is the best invariant predictive distribution. Next, we consider shrinkage methods of constructing predictive distributions. The relation between the main results here and several previous studies on Bayesian theory is discussed from information theoretic viewpoints." @default.
- W1640105997 created "2016-06-24" @default.
- W1640105997 creator A5086251945 @default.
- W1640105997 date "2004-01-01" @default.
- W1640105997 modified "2023-10-18" @default.
- W1640105997 title "Noninformative priors for prediction based on group models" @default.
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- W1640105997 doi "https://doi.org/10.1063/1.1835252" @default.
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