Details
| Original language | English |
|---|---|
| Pages (from-to) | 35-52 |
| Number of pages | 18 |
| Journal | Studia geophysica et geodaetica |
| Volume | 61 |
| Issue number | 1 |
| Early online date | 9 Aug 2016 |
| Publication status | Published - 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.
Keywords
- Bayesian inference, Errors-In-Variables, Maximum Likelihood, Total Least-Squares, informative prior, noninformative prior, quasi solution
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Geophysics
- Earth and Planetary Sciences(all)
- Geochemistry and Petrology
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In: Studia geophysica et geodaetica, Vol. 61, No. 1, 01.2017, p. 35-52.
Research output: Contribution to journal › Article › Research › 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 -