A method for approximating the density of maximum-likelihood and maximum a posteriori estimates under a Gaussian noise model.
A method for approximating the density of maximum-likelihood and maximum a posteriori estimates under a Gaussian noise model.
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DOI：
10.1016/s1361-8415(98)80019-4
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发表时间：
1998-12-01
影响因子：
10.9
通讯作者：
Rybicki, F J
中科院分区：
文献类型：
--
作者：
Abbey, C K;Clarkson, E;Rybicki, F J
在低信噪比的情况下，最大似然(ML)和最大后验概率(MAP)估计在非线性问题中的性能不能很好地用Cramer-Rao或其他方差下界来预测。为了更好地刻画这些条件下ML估计和MAP估计的分布，我们给出了这种估计的条件分布的密度值的点逼近。在一个实例问题中，该近似分布捕获了在存在高斯分布噪声的情况下最大似然估计的分布的基本特征。
The performance of maximum-likelihood (ML) and maximum a posteriori (MAP) estimates in non-linear problems at low data SNR is not well predicted by the Cramer-Rao or other lower bounds on variance. In order to better characterize the distribution of ML and MAP estimates under these conditions, we derive a point approximation to density values of the conditional distribution of such estimates. In an example problem, this approximate distribution captures the essential features of the distribution of ML estimates in the presence of Gaussian-distributed noise.