Open AccessANALISIS REGRESI BAYES LINEAR SEDERHANA DENGAN PRIOR NONINFORMATIF
Author(s) -
Anak Agung Istri Agung Candra Iswari,
I Wayan Sumarjaya,
I Gusti Ayu Made Srinadi
Publication year - 2014
Publication title -
e-jurnal matematika
Language(s) - English
Resource type - Journals
ISSN - 2303-1751
DOI - 10.24843/mtk.2014.v03.i02.p064
Subject(s) - confidence interval , statistics , bayesian probability , mathematics , credible interval , gibbs sampling , bayes' theorem , prior probability
The aim of this study is to apply Bayesian simple linear regression using noninformative prior. The data used in this study is 30 observational data with error generated from normal distribution. The noninformative prior was formed using Jeffreys’ rule. Computation was done using the Gibbs Sampler algorithm with 10.000 iteration. We obtain the following estimates for the parameters, with 95% Bayesian confidence interval (0,775775; 2,626025), with 95% Bayesian confidence interval (2,948; 3,052), and with 95% Bayesian confidence interval (0,375295; 1,114). These values are not very different compared to the actual value of the parameters.