Parameter estimation using extended Bayesian method in tunnelling

2010 ◽  
pp. 197-219
Author(s):  
I M Lee ◽  
D H Kim
Keyword(s):  
Parameter Estimation ◽  
Bayesian Method

Reliability Analysis of Vehicle Equipment Based on Bayesian Method

2012 ◽  
Vol 253-255 ◽  
pp. 2091-2096
Author(s):  
Yan Feng Tang ◽  
Hui Mei Li ◽  
Xiang Kai Liu ◽  
Shao Qing Liu
Keyword(s):  
Parameter Estimation ◽  
Data Processing ◽  
Reliability Analysis ◽  
Bayesian Method ◽  
Distribution Model ◽  
Optimal Distribution ◽  
Model Based ◽  
Selection Of

Bayesian method was introduced and leaded into the vehicle fault data processing. The parameter estimation and the selection of the optimal distribution model based on Bayesian method were studied, and an example was given. The references are provided for the application of Bayesian method in the large complicated systems, such as vehicle equipments.

scholarly journals A modified extended Bayesian method for parameter estimation

2007 ◽  
Vol 12 (5) ◽  
pp. 546-553 ◽  
Author(s):  
Masheng Zhou ◽  
Yiqian Li ◽  
Zhihai Xiang ◽  
G. Swoboda ◽  
Zhangzhi Cen
Keyword(s):  
Parameter Estimation ◽  
Bayesian Method

scholarly journals Estimation of Beta Regression Model with Applied Study

2016 ◽  
Vol 12 (11) ◽  
pp. 6773-6777
Author(s):  
Hanaa Abd El Reheem Salem
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Regression Model ◽  
Bayesian Method ◽  
Numerical Study ◽  
Regression Coefficients ◽  
Beta Regression ◽  
Applied Study ◽  
The Mean ◽  
Beta Regression Model

This paper proposes a regression model where the dependent variable is beta distributed. Therefore the observations of the dependent variable must fall within (0,1) interval. This beta regression model produces two regression coefficients: one for the model of the mean and one for the model of the dispersion. Parameter estimation is performed by maximum likelihood and Bayesian method. Finally, numerical study is presented.

scholarly journals Covid-19 Patient Mortality Risk Classification Using Bayesian Binary Logistic Regression

2021 ◽  
Vol 18 (1) ◽  
pp. 150-160
Author(s):  
Muhammad Qolbi Shobri ◽  
Ferra Yanuar ◽  
Dodi Devianto
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Mortality Rate ◽  
Bayesian Method ◽  
Binary Logistic Regression ◽  
Predictor Variables ◽  
New Disease ◽  
Death Risk ◽  
Risk Of Death ◽  
The World

At the end of 2019 the world was shocked by a new disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). The disease is called Covid-19 (Coronavirus Disease). The mortality rate due to disease is increasing every day. In Indonesia as of April 2021, confirmed Covid-19 patients who died reached 42,530 patients, seeing the high mortality rate of Covid-19 patients so it needs to be studied further so that the risk of death of these Covid-19 patients can be minimized. This research utilizing  binary logistic regression with Bayesian method parameter estimation. In this study, the predictor variables used were in the form of categories that each category in the predictor variables was assumed to have the same risk of death risk of Covid-19 patients. The results of this study indicate that the number of comorbids has a significant effect on the risk of death of Covid-19 patients, the more the number of comorbids suffered by the patient, the higher the risk of death of the patient. The accuracy of this method in classifying data is 84.68%.

Sign in / Sign up

Export Citation Format

Share Document