Bayesian inference for the Errors-In-Variables model
Bayesian inference for the Errors-In-Variables model
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DOI：
10.1007/s11200-015-6107-9
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发表时间：
2016
影响因子：
0.9
通讯作者：
X. Fang;Bofeng Li;H. Alkhatib;W. Zeng;Yibin Yao
中科院分区：
文献类型：
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作者：
X. Fang;Bofeng Li;H. Alkhatib;W. Zeng;Yibin Yao
讨论了基于变量误差模型的贝叶斯推理。所提出的估计不仅为未知参数，而且为方差因子与或没有先验信息。本文提出的未知参数的总体最小二乘（TLS）估计被认为是拟最小二乘（LS）和拟最大后验（MAP）解。此外，EIV模型的方差因子被证明总是小于传统的线性模型的方差因子。一个数值例子证明了所提出的解决方案的性能。
We discuss the Bayesian inference based on the Errors-In-Variables (EIV) model. The proposed estimators are developed not only for the unknown parameters but also for the variance factor with or without prior information. The proposed Total Least-Squares (TLS) estimators of the unknown parameter are deemed as the quasi Least-Squares (LS) and quasi maximum a posterior (MAP) solution. In addition, the variance factor of the EIV model is proven to be always smaller than the variance factor of the traditional linear model. A numerical example demonstrates the performance of the proposed solutions.