Open AccessBayesian Inference on Regression Model with an Unknown Change Point
Author(s) -
Olatunji Oladimeji Ojo
Publication year - 2021
Publication title -
asian journal of probability and statistics
Language(s) - English
Resource type - Journals
ISSN - 2582-0230
DOI - 10.9734/ajpas/2021/v13i230305
Subject(s) - inference , bayesian inference , gibbs sampling , bayesian probability , bayesian linear regression , regression , point estimation , posterior probability , point (geometry) , regression analysis , change detection , statistics , mathematics , econometrics , computer science , artificial intelligence , geometry
In this work, we describe a Bayesian procedure for detection of change-point when we have an unknown change point in regression model. Bayesian approach with posterior inference for change points was provided to know the particular change point that is optimal while Gibbs sampler was used to estimate the parameters of the change point model. The simulation experiments show that all the posterior means are quite close to their true parameter values. The performance of this method is recommended for multiple change points.