TY - JOUR
ID - 13828
TI - A Simple Gibbs Sampler for Learning Bayesian Network Structure
JO - Journal of Data Science and Modeling
JA - JDSM
LA - en
SN - 2676-5926
AU - Rezaei Tabar, Vahid
AD - Department of Statistics, Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabataba&#039;i University, Tehran, Iran
Y1 - 2023
PY - 2023
VL - 1
IS - 2
SP - 87
EP - 97
KW - Bayesian Network
KW - Gibbs Sampling
KW - Burn-in graphs
DO - 10.22054/jcsm.2021.55657.1022
N2 - 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.
UR - https://jdscm.atu.ac.ir/article_13828.html
L1 - https://jdscm.atu.ac.ir/article_13828_ce720d13c735fc3471d0d71682da09f3.pdf
ER -