Open AccessBayesian Computational Methods of the Logistic Regression Model
Author(s) -
Najla A. Al-Khairullah,
Tasnim H.K. Al-Baldawi
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 - 10.1088/1742-6596/1804/1/012073
Subject(s) - markov chain monte carlo , logistic regression , hybrid monte carlo , computer science , monte carlo method , bayesian probability , metropolis–hastings algorithm , random walk , algorithm , machine learning , artificial intelligence , statistics , econometrics , mathematics
In this paper, we will discuss the performance of Bayesian computational approaches for estimating the parameters of a Logistic Regression model. Markov Chain Monte Carlo (MCMC) algorithms was the base estimation procedure. We present two algorithms: Random Walk Metropolis (RWM) and Hamiltonian Monte Carlo (HMC). We also applied these approaches to a real data set.