Open AccessThe Selection of Bayesian Polynomial Regression with INLA by using DIC, WAIC and CPO
Author(s) -
Hery Sutanto,
Henny Pramoedyo,
Wayan Surya Wardhani,
Suci Astutik
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1747/1/012029
Subject(s) - polynomial regression , bayesian linear regression , polynomial , model selection , bayesian probability , linear regression , regression analysis , mathematics , selection (genetic algorithm) , regression , statistics , polynomial and rational function modeling , bayesian information criterion , bayesian inference , computer science , artificial intelligence , mathematical analysis
The polynomial regression model is extended the multiple linear regression. The selection of Bayesian polynomial regression model with INLA required Criterion. Criterion is using the measure fit model with the available data. There are three criteria, namely DIC, WAIC and CPO. The smaller criterion value from DIC, WAIC and CPO on a model show the best Bayesian polynomial regression model with INLA.