A Simple Gibbs Sampler for learning Bayesian Network Structure
عنوان مقاله: A Simple Gibbs Sampler for learning Bayesian Network Structure
شناسه ملی مقاله: JR_JCSM-1-2_005
منتشر شده در در سال 1400
شناسه ملی مقاله: JR_JCSM-1-2_005
منتشر شده در در سال 1400
مشخصات نویسندگان مقاله:
Vahid Rezaei Tabar - Department of Statistics, Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabataba&#۰۳۹;i University, Tehran, Iran
خلاصه مقاله:
Vahid Rezaei Tabar - Department of Statistics, Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabataba&#۰۳۹;i University, Tehran, Iran
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.
کلمات کلیدی: Bayesian Network, Gibbs Sampling, Burn-in graphs
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1392879/