Bayesian inference for the Errors-In-Variables model

被引：26

作者：

Fang, Xing ^{[1
,2
]}

Li, Bofeng ^{[3
]}

Alkhatib, Hamza ^{[4
]}

Zeng, Wenxian ^{[1
]}

Yao, Yibin ^{[1
]}

机构：

[1] Wuhan Univ, Sch Geodesy & Geomat, Wuhan, Peoples R China

[2] Ohio State Univ, Sch Earth Sci, Columbus, OH 43210 USA

[3] Tongji Univ, Coll Surveying & Geoinformat, Shanghai, Peoples R China

[4] Leibniz Univ Hannover, Geodet Inst, Hannover, Germany

来源：

STUDIA GEOPHYSICA ET GEODAETICA | 2017年 / 61卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Errors-In-Variables; Total Least-Squares; Bayesian inference; quasi solution; Maximum Likelihood; noninformative prior; informative prior; TOTAL LEAST-SQUARES; TRANSFORMATION; REGRESSION; ADJUSTMENT; PARAMETERS; VARIANCE;

D O I：

10.1007/s11200-015-6107-9

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

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.

引用

页码：35 / 52

页数：18

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