Negative numbers are essential in mathematics. They are not needed to describe statistical experiments, as those are expressed in terms of positive probabilities. Shannon information was firstly defined for characterizing informational uncertainty of classical probabilistic distributions. However, it is unknown why there is negative information for more than two random variables on finite sample spaces. We first show the negative Shannon mutual information of three random variables implies Bayesian network representations of its joint distribution. We then show the intrinsic compatibility with negative Shannon information is generic for Bayesian networks with quantum realizations. This further suggests a new kind of space-dependent nonlocality. The present result provides a device-independent witness of negative Shannon information.
翻译:负数在数学中是必不可少的。 负数并不需要用来描述统计实验, 因为这些实验是以正概率表示的。 香农信息首先被定义为将典型概率分布的信息不确定性定性为典型概率分布的信息不确定性。 但是,尚不清楚为什么在有限的抽样空间上有两个以上的随机变量存在负面信息。 我们首先显示三个随机变量的阴性香农相互信息意味着贝叶斯网络对其联合分布的表示。 我们然后显示与阴性香农信息的内在兼容性是巴伊西亚网络在量子上实现的通用信息。 这进一步表明一种新的空间依赖性非地性。 目前的结果为负面香农信息提供了一个独立设备证人。