基于最优偏差函数的贝叶斯估计界

2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI:10.1109/CAMSAP.2007.4497961

Z. Ben-Haim, Yonina C. Eldar

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引用次数: 1

摘要

研究了贝叶斯估计问题中最小均方误差的下界问题。将基于确定最优偏置函数的Young和Westerberg的界推广到向量参数的情况。数值研究表明，与其他方法相比，该边界更紧凑，计算更简单。

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A Bayesian Estimation Bound based on the Optimal Bias Function

We consider the problem of finding a lower bound on the minimum mean-squared error in a Bayesian estimation problem. The bound of Young and Westerberg, which is based on determining the optimal bias function, is extended to the case of a vector parameter. A numerical study demonstrates that the bound is both tighter and simpler to compute than alternative techniques.

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来源期刊

2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

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