ANALISIS REGRESI BAYES LINEAR SEDERHANA DENGAN PRIOR NONINFORMATIF
Abstract
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.
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Published
2014-05-31
How to Cite
ISWARI, ANAK AGUNG ISTRI AGUNG CANDRA; SUMARJAYA, I WAYAN; SRINADI, I GUSTI AYU MADE.
ANALISIS REGRESI BAYES LINEAR SEDERHANA DENGAN PRIOR NONINFORMATIF.
E-Jurnal Matematika, [S.l.], v. 3, n. 2, p. 38 - 44, may 2014.
ISSN 2303-1751. Available at: <https://ojs.unud.ac.id/index.php/mtk/article/view/9604>. Date accessed: 21 nov. 2024.
doi: https://doi.org/10.24843/MTK.2014.v03.i02.p064.
Issue
Section
Articles
Keywords
Bayesian regression; noninformative prior; Jeffreys’ rule; the Gibbs Sampler algorithm
This work is licensed under a Creative Commons Attribution 4.0 International License.
