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A Simple Gibbs Sampler for learning Bayesian Network Structure | ||
| Journal of Computational Statistics and Modeling | ||
| دوره 1، شماره 2، اسفند 2021، صفحه 87-97 اصل مقاله (386.45 K) | ||
| نوع مقاله: origenal | ||
| شناسه دیجیتال (DOI): 10.22054/jcsm.2021.55657.1022 | ||
| نویسنده | ||
Vahid Rezaei Tabar 
| ||
| Department of Statistics, Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabataba'i University, Tehran, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Bayesian Network؛ Gibbs sampling؛ Burn-in graphs | ||
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آمار تعداد مشاهده مقاله: 26 تعداد دریافت فایل اصل مقاله: 85 |
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