Bayesian inference for the Errors-In-Variables model

Verfasst von

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.

Details

Organisationseinheit(en): Geodätisches Institut
Externe Organisation(en): Wuhan University
The Ohio State University
Tongji University
Typ: Artikel
Journal: Studia geophysica et geodaetica
Band: 61
Seiten: 35-52
Anzahl der Seiten: 18
ISSN: 0039-3169
Publikationsdatum: 01.2017
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Geophysik, Geochemie und Petrologie
Elektronische Version(en): https://doi.org/10.1007/s11200-015-6107-9 (Zugang: Geschlossen )
: Details im Forschungsportal „Research@Leibniz University“