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- W2963696145 abstract "One-step ahead prediction for the multinomial model is considered. The performance of a predictive density is evaluated by the average Kullback-Leibler divergence from the true density to the predictive density. Asymptotic approximations of risk functions of Bayesian predictive densities based on Dirichlet priors are obtained. It is shown that a Bayesian predictive density based on a specific Dirichlet prior is asymptotically minimax. The asymptotically minimax prior is different from known objective priors such as the Jeffreys prior or the uniform prior." @default.
- W2963696145 created "2019-07-30" @default.
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- W2963696145 date "2012-01-01" @default.
- W2963696145 modified "2023-09-26" @default.
- W2963696145 title "Asymptotically minimax Bayesian predictive densities for multinomial models" @default.
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- W2963696145 doi "https://doi.org/10.1214/12-ejs700" @default.
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