The aim of this paper is to learn a Bayesian network structure for discrete variables. For this purpose, we introduce a Gibbs sampler method. Each sample represents a Bayesian network. Thus, in the process of Gibbs sampling, we obtain a set of Bayesian networks. For achieving a single graph that represents the best graph fitted on data, we use the mode of burn-in graphs. This means that the most frequent edges of burn-in graphs are considered to indicate the best single graph. The results on the well-known Bayesian networks show that our method has higher accuracy in the task of learning a Bayesian network structure.
Rezaei Tabar,V . (2023). A Simple Gibbs Sampler for Learning Bayesian Network Structure. Journal of Data Science and Modeling, 1(2), 87-97. doi: 10.22054/jcsm.2021.55657.1022
MLA
Rezaei Tabar,V . "A Simple Gibbs Sampler for Learning Bayesian Network Structure", Journal of Data Science and Modeling, 1, 2, 2023, 87-97. doi: 10.22054/jcsm.2021.55657.1022
HARVARD
Rezaei Tabar V. (2023). 'A Simple Gibbs Sampler for Learning Bayesian Network Structure', Journal of Data Science and Modeling, 1(2), pp. 87-97. doi: 10.22054/jcsm.2021.55657.1022
CHICAGO
V Rezaei Tabar, "A Simple Gibbs Sampler for Learning Bayesian Network Structure," Journal of Data Science and Modeling, 1 2 (2023): 87-97, doi: 10.22054/jcsm.2021.55657.1022
VANCOUVER
Rezaei Tabar V. A Simple Gibbs Sampler for Learning Bayesian Network Structure. JDSM. 2023;1(2):87-97. doi: 10.22054/jcsm.2021.55657.1022
