有界正态均值的估计:最不利先验离散性的另一种证明

2019 IEEE Information Theory Workshop (ITW) Pub Date : 2019-08-01 DOI:10.1109/ITW44776.2019.8988927

S. Yagli, Alex Dytso, H. Poor

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引用次数: 3

摘要

本文研究了经典贝叶斯正态均值估计问题，该问题假设估计包含在有界集合中。已知该均值估计问题的最不利分布是具有有限多个质量点的离散分布。这项工作提供了利用高斯核的变分递减性质的另一种证明。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Estimation of Bounded Normal Mean: An Alternative Proof for the Discreteness of the Least Favorable Prior

This paper studies the classical Bayesian normal mean estimation problem where the estimand is assumed to be contained in a bounded set. It is known that the least favorable distribution for this mean estimation problem is discrete with finitely many mass points. This work offers an alternative proof utilizing the variational diminishing property of Gaussian kernels.

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2019 IEEE Information Theory Workshop (ITW)

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