A COMPOUND POISSON MODEL FOR LEARNING DISCRETE BAYESIAN NETWORKS

作     者：Abdelaziz GHRIBI Afif MASMOUDI 

作者机构：Laboratory of Physic-MathematicsUniversity of Sfax Laboratory of Probability and StatisticsUniversity of Sfax 

出 版 物：《Acta Mathematica Scientia》 (数学物理学报（B辑英文版）)

年 卷 期：2013年第33卷第6期

页      面：1767-1784页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

主　　题：Bayesian network compound Poisson distribution multinomial distribution implicit approach mobile communication networks 

摘      要：We introduce here the concept of Bayesian networks, in compound Poisson model, which provides a graphical modeling framework that encodes the joint probability distribution for a set of random variables within a directed acyclic graph. We suggest an approach proposal which offers a new mixed implicit estimator. We show that the implicit approach applied in compound Poisson model is very attractive for its ability to understand data and does not require any prior information. A comparative study between learned estimates given by implicit and by standard Bayesian approaches is established. Under some conditions and based on minimal squared error calculations, we show that the mixed implicit estimator is better than the standard Bayesian and the maximum likelihood estimators. We illustrate our approach by considering a simulation study in the context of mobile communication networks.