贝叶斯网络的条件概率生成：简化知识获取问题
Generating Conditional Probabilities for Bayesian Networks: Easing the Knowledge Acquisition Problem
摘要：
在贝叶斯网络中填充条件概率表（CPT）所需的概率分布数量随着与该表关联的父节点数量呈指数级增长。如果表格是通过从领域专家那里得到的知识来填充的，那么任务的绝对数量就会形成相当大的认知障碍。在本文中，我们设计了一个算法来填充CPT，同时缓解知识获取的程度。该算法的输入由一组权重和一组概率分布组成，权重量化了父节点对子节点的影响的相对强度，概率分布的数量仅随相关父节点的数量线性增长。这些都是从领域专家那里得到的。概率集是通过考虑专家在得出概率估计时所使用的统计学来获得的。该算法是用来填充CPT计算适当的加权和引起的分布。我们调用信息几何的方法来演示这些加权和如何捕捉专家的判断策略。
英文摘要：
The number of probability distributions required to populate a conditional probability table (CPT) in a Bayesian network, grows exponentially with the number of parent-nodes associated with that table. If the table is to be populated through knowledge elicited from a domain expert then the sheer magnitude of the task forms a considerable cognitive barrier. In this paper we devise an algorithm to populate the CPT while easing the extent of knowledge acquisition. The input to the algorithm consists of a set of weights that quantify the relative strengths of the influences of the parent-nodes on the child-node, and a set of probability distributions the number of which grows only linearly with the number of associated parent-nodes. These are elicited from the domain expert. The set of probabilities are obtained by taking into consideration the heuristics that experts use while arriving at probabilistic estimations. The algorithm is used to populate the CPT by computing appropriate weighted sums of the elicited distributions. We invoke the methods of information geometry to demonstrate how these weighted sums capture the expert's judgemental strategy.
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