论文标题
我们真的在基于模型的诊断中采样吗？
Do We Really Sample Right In Model-Based Diagnosis?
论文作者
论文摘要
为了代表，统计样本必须以随机和公正的方式从人群中获取。然而，在基于模型的诊断领域的常见做法是从（偏见）最佳第一样本进行估计。一个例子是计算有缺陷系统的一些最可能的故障解释，并使用这些解释来评估系统的哪个方面（如果测量）将带来最高的信息增益。 在这项工作中，我们仔细检查了这些在统计学上没有良好的惯例，即诊断研究人员和从业人员已经遵守了数十年的习惯，这确实是合理的。为此，我们通过经验分析了产生故障解释的各种抽样方法。我们研究了产生的样本的代表性，这些样本的估计是对故障解释以及它们指导诊断决策的能力，以及我们研究样本量的影响，样本量的影响，采样效率和有效性之间的最佳权衡以及如何近似采样技术与精确的采样技术相比。
Statistical samples, in order to be representative, have to be drawn from a population in a random and unbiased way. Nevertheless, it is common practice in the field of model-based diagnosis to make estimations from (biased) best-first samples. One example is the computation of a few most probable possible fault explanations for a defective system and the use of these to assess which aspect of the system, if measured, would bring the highest information gain. In this work, we scrutinize whether these statistically not well-founded conventions, that both diagnosis researchers and practitioners have adhered to for decades, are indeed reasonable. To this end, we empirically analyze various sampling methods that generate fault explanations. We study the representativeness of the produced samples in terms of their estimations about fault explanations and how well they guide diagnostic decisions, and we investigate the impact of sample size, the optimal trade-off between sampling efficiency and effectivity, and how approximate sampling techniques compare to exact ones.
