Harmonic Bayesian Prediction Under α -Divergence
Harmonic Bayesian Prediction Under α -Divergence
复制标题
α 散度下的谐波贝叶斯预测
DOI：
10.1109/tit.2019.2915245
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发表时间：
2019
影响因子：
2.5
通讯作者：
Toshio Ohnishi
中科院分区：
文献类型：
--
作者：
Yuzo Maruyama;Takeru Matsuda;Toshio Ohnishi
我们研究贝叶斯收缩方法构建预测分布。我们认为，多元正态模型与已知的协方差矩阵，并表明贝叶斯预测密度相对于斯坦的谐波先验占主导地位的最佳不变贝叶斯预测密度时，尺寸大于或等于3。从真实分布到预测分布的α发散被采用作为损失函数。
We investigate Bayesian shrinkage methods for constructing predictive distributions. We consider the multivariate normal model with a known covariance matrix and show that the Bayesian predictive density with respect to Stein's harmonic prior dominates the best invariant Bayesian predictive density when the dimension is greater than or equal to 3. Alpha divergence from the true distribution to a predictive distribution is adopted as a loss function.