Bayesian Computational Methods of the Logistic Regression Model

Bayesian Computational Methods of the Logistic Regression Model

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 Mont...

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Veröffentlicht in:Journal of physics. Conference series 2021-02, Vol.1804 (1), p.12073
Hauptverfasser: Al-Khairullah, Najla A., Al-Baldawi, Tasnim H. K.
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Bayesian analysis
Markov chains
Parameter estimation
Physics
Random walk
Regression models
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Beschreibung
Zusammenfassung: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1804/1/012073