ANALISIS REGRESI BAYES LINEAR SEDERHANA DENGAN PRIOR NONINFORMATIF

  • ANAK AGUNG ISTRI AGUNG CANDRA ISWARI Faculty of Mathematics and Natural Sciences, Udayana University
  • I WAYAN SUMARJAYA Faculty of Mathematics and Natural Sciences, Udayana University
  • I GUSTI AYU MADE SRINADI Faculty of Mathematics and Natural Sciences, Udayana University

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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Author Biographies

ANAK AGUNG ISTRI AGUNG CANDRA ISWARI, Faculty of Mathematics and Natural Sciences, Udayana University

Jurusan Matematika FMIPA Universitas Udayana, Bukit Jimbaran-Bali

I WAYAN SUMARJAYA, Faculty of Mathematics and Natural Sciences, Udayana University

Jurusan Matematika FMIPA Universitas Udayana, Bukit Jimbaran-Bali

I GUSTI AYU MADE SRINADI, Faculty of Mathematics and Natural Sciences, Udayana University

Jurusan Matematika FMIPA Universitas Udayana, Bukit Jimbaran-Bali

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: 20 aug. 2022. doi: https://doi.org/10.24843/MTK.2014.v03.i02.p064.
Section
Articles

Keywords

Bayesian regression; noninformative prior; Jeffreys’ rule; the Gibbs Sampler algorithm

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