论文标题
在非结构化概率模型中隐藏独立性
Hidden independence in unstructured probabilistic models
论文作者
论文摘要
我们描述了一种新颖的方式来表示随机二进制字符串的概率分布为具有与独立（尽管不一定相同分布）bernoulli字符相关的最大加权组件的混合物。我们将其称为产生字符串的概率源的潜在独立权重，并得出一个组合算法来计算它。我们提出的分解可以作为假设检验的布尔范式的替代方法，或者评估来自具有独立边缘源的源的未腐败样品的比例。从这个意义上讲，潜在的独立权重量化了概率来源中包含的最大独立性，而概率来源可能没有独立的边缘。
We describe a novel way to represent the probability distribution of a random binary string as a mixture having a maximally weighted component associated with independent (though not necessarily identically distributed) Bernoulli characters. We refer to this as the latent independent weight of the probabilistic source producing the string, and derive a combinatorial algorithm to compute it. The decomposition we propose may serve as an alternative to the Boolean paradigm of hypothesis testing, or to assess the fraction of uncorrupted samples originating from a source with independent marginals. In this sense, the latent independent weight quantifies the maximal amount of independence contained within a probabilistic source, which, properly speaking, may not have independent marginals.
