Most Relevant Explanation in Bayesian Networks
Most Relevant Explanation in Bayesian Networks
复制标题
贝叶斯网络中最相关的解释
DOI：
10.1613/jair.3301
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Tsai
中科院分区：
文献类型：
--
作者：
Changhe Yuan;Heejin Lim;Tsai
贝叶斯网络中的一个主要推理任务是解释为什么使用一组目标变量可以观察到某些变量处于特定状态。解决这个问题的现有方法通常会产生过于简单（未指定）或过于复杂（过度指定）的解释。在本文中，我们介绍了一种方法，称为最相关的解释（MRE），找到一个部分实例化的目标变量，最大化广义贝叶斯因子（GBF）的最佳解释为给定的证据。我们的研究表明，GBF有几个理论属性，使MRE自动识别最相关的目标变量，形成其解释。特别是，条件贝叶斯因子（CBF），定义为GBF的一个新的解释条件下现有的解释，提供了一个软措施的相关程度的变量在新的解释解释现有的解释解释解释的证据。因此，MRE能够自动从其解释中删除不太相关的变量。我们还表明，CBF是能够很好地捕捉到的解释了现象，往往是在贝叶斯网络。此外，我们定义了两个候选解决方案之间的优势关系，并使用关系推广MRE找到一组顶级的解释，这是多样性和代表性。几个基准诊断贝叶斯网络的案例研究表明，MRE往往能够找到解释性的假设，不仅准确，而且简洁。
A major inference task in Bayesian networks is explaining why some variables are observed in their particular states using a set of target variables. Existing methods for solving this problem often generate explanations that are either too simple (underspecified) or too complex (overspecified). In this paper, we introduce a method called Most Relevant Explanation (MRE) which finds a partial instantiation of the target variables that maximizes the generalized Bayes factor (GBF) as the best explanation for the given evidence. Our study shows that GBF has several theoretical properties that enable MRE to automatically identify the most relevant target variables in forming its explanation. In particular, conditional Bayes factor (CBF), defined as the GBF of a new explanation conditioned on an existing explanation, provides a soft measure on the degree of relevance of the variables in the new explanation in explaining the evidence given the existing explanation. As a result, MRE is able to automatically prune less relevant variables from its explanation. We also show that CBF is able to capture well the explaining-away phenomenon that is often represented in Bayesian networks. Moreover, we define two dominance relations between the candidate solutions and use the relations to generalize MRE to find a set of top explanations that is both diverse and representative. Case studies on several benchmark diagnostic Bayesian networks show that MRE is often able to find explanatory hypotheses that are not only precise but also concise.
DOI：
10.1016/0010-4809(81)90012-4
发表时间：
1981-01-01
期刊：
COMPUTERS AND BIOMEDICAL RESEARCH
影响因子：
--
作者：
TEACH, RL;SHORTLIFFE, EH
通讯作者：
SHORTLIFFE, EH