Allameh Tabatabaâ€™i University PressJournal of Data Science and Modeling2676-59261220230601A Simple Gibbs Sampler for Learning Bayesian Network Structure87971382810.22054/jcsm.2021.55657.1022ENVahidRezaei TabarDepartment of Statistics, Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabataba&#039;i University, Tehran, IranJournal Article20200927The 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.https://jdscm.atu.ac.ir/article_13828_ce720d13c735fc3471d0d71682da09f3.pdf