A Simple Gibbs Sampler for learning Bayesian Network Structure

Document Type : origenal

Author

Department of Statistics, Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabataba'i University, Tehran, Iran

10.22054/jcsm.2021.55657.1022

Abstract

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.

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