Twofold Structure of Duality in Bayesian Model Averaging

DOI Web Site Web Site 参考文献43件

この論文をさがす

抄録

Two Bayesian prediction problems in the context of model averaging are investigated by adopting dual Kullback-Leibler divergence losses, the e-divergence and the m-divergence losses. We show that the optimal predictors under the two losses are shown to satisfy interesting saddlepoint-type equalities. Actually, the optimal predictor under the e-divergence loss balances the log-likelihood ratio and the loss, while the optimal predictor under the m-divergence loss balances the Shannon entropy difference and the loss. These equalities also hold for the predictors maximizing the log-likelihood and the Shannon entropy respectively under the e-divergence loss and the m-divergence loss, showing that enlarging the log-likelihood and the Shannon entropy moderately will lead to the optimal predictors. In each divergence loss case we derive a robust predictor in the sense that its posterior risk is constant by minimizing a certain convex function. The Legendre transformation induced by this convex function implies that there is inherent duality in each Bayesian prediction problem.

収録刊行物

参考文献 (43)*注記

もっと見る

関連プロジェクト

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ