Anand Vibha, Downs Stephen M
Children's Health Services Research, Indiana University School of Medicine.
AMIA Annu Symp Proc. 2010 Nov 13;2010:16-20.
In 2004, an extension of the Noisy-OR formalism termed the Recursive Noisy-OR (RNOR) rule was published for estimating complex probabilistic interactions in a Bayesian Network (BN). The RNOR rule presents an algorithm to construct a complete conditional probability distribution (CPD) of a node while allowing domain causal relationships over and above causal independence to be tractably captured in a semantically meaningful way. However, to the best of our knowledge, the accuracy of this rule has not been tested empirically. In this paper, we report the results of a study that compares the performance of a data-trained expert BN (empiric BN) with the reformulated BN, using the RNOR rule. The original empiric BN was trained with a large dataset from the Regenstrief Medical Record System (RMRS). Furthermore, we evaluate conditions in our dataset which render the RNOR rule inapplicable and discuss our use of Noisy-OR calculations in such situations. We call this approach "Adaptive Recursive Noisy-OR".
2004年，一种名为递归噪声或（RNOR）规则的噪声或形式主义扩展被发表，用于估计贝叶斯网络（BN）中的复杂概率交互作用。RNOR规则提出了一种算法，用于构建节点的完整条件概率分布（CPD），同时允许以语义有意义的方式以易于处理的方式捕捉因果独立性之上的领域因果关系。然而，据我们所知，该规则的准确性尚未经过实证检验。在本文中，我们报告了一项研究的结果，该研究使用RNOR规则比较了数据训练的专家BN（经验BN）与重新制定的BN的性能。原始的经验BN是使用来自雷根斯特里夫医疗记录系统（RMRS）的大型数据集进行训练的。此外，我们评估了数据集中使RNOR规则不适用的条件，并讨论了在此类情况下我们对噪声或计算的使用。我们将这种方法称为“自适应递归噪声或”。
