Bayesian inference for the Errors-In-Variables model

authored by: Xing Fang, Bofeng Li, Hamza Alkhatib, Wenxian Zeng, Yibin Yao
Abstract: 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.
Organisation(s): Geodetic Institute
External Organisation(s): Wuhan University
The Ohio State University
Tongji University
Type: Article
Journal: Studia geophysica et geodaetica
Volume: 61
Pages: 35-52
No. of pages: 18
ISSN: 0039-3169
Publication date: 01.2017
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Geophysics, Geochemistry and Petrology
Electronic version(s): https://doi.org/10.1007/s11200-015-6107-9 (Access: Closed)
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