%0 Journal Article
%T A Simple Gibbs Sampler for Learning Bayesian Network Structure
%J Journal of Data Science and Modeling
%I Allameh Tabatabaâ€™i University Press
%Z 2676-5926
%A Rezaei Tabar, Vahid
%D 2023
%\ 06/01/2023
%V 1
%N 2
%P 87-97
%! A Simple Gibbs Sampler for Learning Bayesian Network Structure
%K Bayesian Network
%K Gibbs Sampling
%K Burn-in graphs
%R 10.22054/jcsm.2021.55657.1022
%X 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.
%U https://jdscm.atu.ac.ir/article_13828_ce720d13c735fc3471d0d71682da09f3.pdf