论文标题
来自图表上嘈杂选民模型的本地观察的全局信息
Global information from local observations of the noisy voter model on a graph
论文作者
论文摘要
我们在图的一个顶点处观察到离散时间嘈杂的选民模型的结果。我们表明，某些图可以通过观测顺序中的重复频率来区分。我们证明该统计量在渐近上是正常的，并且几乎（渐近地）几乎所有有限图。我们猜想嘈杂的选民模型将除恒星以外的任何两个图之间区分。
We observe the outcome of the discrete time noisy voter model at a single vertex of a graph. We show that certain pairs of graphs can be distinguished by the frequency of repetitions in the sequence of observations. We prove that this statistic is asymptotically normal and that it distinguishes between (asymptotically) almost all pairs of finite graphs. We conjecture that the noisy voter model distinguishes between any two graphs other than stars.
