Abstract Credal networks generalize Bayesian networks to allow for imprecision in probability values. This paper reviews the main results on credal networks under strong independence, as there has been significant progress in the literature during the last decade or so. We focus on computational aspects, summarizing the main algorithms and complexity results for inference and decision making. We address the question “What is really known about strong extensions of credal networks?” by looking at theoretical results and by presenting a short summary of real applications.

中文翻译：

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Credal Networks 三十年：规范、算法和复杂性

Abstract Credal 网络概括了贝叶斯网络，以允许概率值的不精确性。本文回顾了强独立性下信用网络的主要结果，因为过去十年左右的文献取得了重大进展。我们专注于计算方面，总结了推理和决策的主要算法和复杂性结果。我们解决了这样一个问题：“关于信任网络的强大扩展，人们真正了解什么？” 通过查看理论结果和对实际应用的简短总结。