Assessing the Weighted Sum Algorithm for Automatic Generation of Probabilities in Bayesian Networks

摘要：

A Bayesian Network (BN) is a probabilistic reasoning technique，which to date has been used in a broad range of applications．One of the key challenges in constructing a BN is obtaining its Conditional Probability Tables(CPTs)．CPTs can be learnt from data(when available)，elicited from domain experts．or a combination of both．Eliciting from domain experts provides more flexibility；however， CPTs grow in size exponentially，thus making their elicitation process very time consuming and costly．Previous work proposed a solution to this problem using the weighted sam algorithm(WSA)9；however no empirical results were given on the algorithm’s elicitation reduction and prediction accuracy．Hence the aim of this paper is to present two empirical studies that assess the WSA’s efficiency and prediction accuracy． Onr results show that the estimates obtained using the WSA were highly accurate and make significant reductions in elicitation．

关键词： Bayesian Network Conditional Probability Weighted Sum Algorithm Knowledge Elicitation CPT Elicitation Empirical study

作者: Simon Baker Emilia Mendes

作者单位: The University of Auckland Computer Science Department Private Bag 92019, Auckland, New Zealand The University of Auckand Computer Science Department Proivate Bag 92019, Auckland, New Zealand

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-7

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）