Eliciting prior information to enhance the predictive performance of Bayesian graphical models
Eliciting prior information to enhance the predictive performance of Bayesian graphical models
复制标题
提取先验信息以增强贝叶斯图模型的预测性能
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
A. Raftery
中科院分区：
文献类型：
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作者：
D. Madigan;J. Gavrin;A. Raftery
基于知识的系统和统计模型通常都关注对未来可观测数据的预测。在这里，我们专注于评估预测性能，并提供两种技术，以提高贝叶斯图形模型的预测性能。首先，我们提出了贝叶斯模型平均，一种技术占模型的不确定性。其次，我们描述了一种从领域专家那里获取竞争模型先验分布的技术。我们探讨的预测性能的两种技术的背景下，泌尿诊断问题。
Both knowledge-based systems and statistical models are typically concerned with making predictions about future observables. Here we focus on assessment of predictive performance and provide two techniques for improving the predictive performance of Bayesian graphical models. First, we present Bayesian model averaging, a technique for accounting for model uncertainty. Second, we describe a technique for eliciting a prior distribution for competing models from domain experts. We explore the predictive performance of both techniques in the context of a urological diagnostic problem.