Details
Originalsprache | Englisch |
---|---|
Seiten (von - bis) | 35-52 |
Seitenumfang | 18 |
Fachzeitschrift | Studia geophysica et geodaetica |
Jahrgang | 61 |
Ausgabenummer | 1 |
Frühes Online-Datum | 9 Aug. 2016 |
Publikationsstatus | Veröffentlicht - Jan. 2017 |
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.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Geophysik
- Erdkunde und Planetologie (insg.)
- Geochemie und Petrologie
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in: Studia geophysica et geodaetica, Jahrgang 61, Nr. 1, 01.2017, S. 35-52.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Bayesian inference for the Errors-In-Variables model
AU - Fang, Xing
AU - Li, Bofeng
AU - Alkhatib, Hamza
AU - Zeng, Wenxian
AU - Yao, Yibin
PY - 2017/1
Y1 - 2017/1
N2 - 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.
AB - 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.
KW - Bayesian inference
KW - Errors-In-Variables
KW - Maximum Likelihood
KW - Total Least-Squares
KW - informative prior
KW - noninformative prior
KW - quasi solution
UR - http://www.scopus.com/inward/record.url?scp=84981290356&partnerID=8YFLogxK
U2 - 10.1007/s11200-015-6107-9
DO - 10.1007/s11200-015-6107-9
M3 - Article
VL - 61
SP - 35
EP - 52
JO - Studia geophysica et geodaetica
JF - Studia geophysica et geodaetica
SN - 0039-3169
IS - 1
ER -