A comparison of Bayesian analysis methods for reliability parameter estimation in PSA

2010 ◽  
Author(s):  
A.B. El-Shanawany
Keyword(s):  
Parameter Estimation ◽  
Bayesian Analysis ◽  
Reliability Parameter ◽  
Analysis Methods

Estimation of Failure Probability and its Applications in Reliability Data Analysis

2010 ◽  
Vol 118-120 ◽  
pp. 601-605
Author(s):  
Han Ming
Keyword(s):  
Parameter Estimation ◽  
Data Analysis ◽  
Bayesian Estimation ◽  
Failure Probability ◽  
Evaluation Method ◽  
Estimation Method ◽  
New Method ◽  
Reliability Parameter ◽  
Hierarchical Bayesian Estimation ◽  
Definition Of

Evaluation method of reliability parameter estimation needs to be improved effectively with the advance of science and technology. This paper develops a new method of parameter estimation, which is named E-Bayesian estimation method. In the case one hyper-parameter, the definition of E-Bayesian estimation of the failure probability is provided, moreover, the formulas of E-Bayesian estimation and hierarchical Bayesian estimation, and the property of E-Bayesian estimation of the failure probability are also provided. Finally, calculation on practical problems shows that the provided method is feasible and easy to perform.

scholarly journals Bayesian analysis of galaxy spectral energy distributions with BayeSED

2012 ◽  
Vol 8 (S295) ◽  
pp. 312-312
Author(s):  
Yunkun Han ◽  
Zhanwen Han
Keyword(s):  
Parameter Estimation ◽  
Complex Systems ◽  
Bayesian Analysis ◽  
Bayesian Model ◽  
Model Comparison ◽  
Spectral Energy ◽  
Infrared Galaxies ◽  
Energy Distributions ◽  
Bayesian Model Comparison ◽  
Rigorous Test

AbstractIn Han & Han (2012), we have preliminarily built BayeSED and applied it to a sample of hyperluminous infrared galaxies. The physically reasonable results obtained from Bayesian model comparison and parameter estimation show that BayeSED could be a useful tool for understanding the nature of complex systems, such as dust obscured starburst-AGN composite galaxies, from decoding their complex SEDs. In this contribution, we present a more rigorous test of BayeSED by making a mock catalog from model SEDs with the value of all parameters to be known in advance.

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