Abstract We test the accuracy of various methods for approximating underspecified joint probability distributions. In particular, we examine the maximum entropy and the analytic center approximations, and we introduce three methods for approximating a discrete joint probability distribution given partial probabilistic information. Our results suggest that recently proposed approximations and our new approximations more accurately represent the possible uncertainty models than do previous models such as maximum entropy.

中文翻译：

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探索给定部分信息的联合分布近似的准确性

摘要 我们测试了用于逼近未指定联合概率分布的各种方法的准确性。特别是，我们检查了最大熵和解析中心近似值，并介绍了三种方法来逼近给定部分概率信息的离散联合概率分布。我们的结果表明，最近提出的近似值和我们的新近似值比以前的模型（如最大熵）更准确地表示可能的不确定性模型。