指数族随机图模型的一种完美抽样方法
A Perfect Sampling Method for Exponential Family Random Graph Models
摘要：
生成具有非平凡边依赖的随机图模型是一个越来越重要的问题。在这里，我们介绍了一种方法，允许完美的抽样随机图模型的指数族形式（“指数族随机图”模型），使用一个变种的耦合从过去。我们说明了使用的方法，通过应用程序的马尔可夫图，一个家庭，一直是相当多的研究的主题。我们还展示了如何将该方法应用到一个变体的偏置网络模型，这不是指数参数化。
英文摘要：
Generation of deviates from random graph models with non-trivial edge dependence is an increasingly important problem. Here, we introduce a method which allows perfect sampling from random graph models in exponential family form ("exponential family random graph" models), using a variant of Coupling From The Past. We illustrate the use of the method via an application to the Markov graphs, a family that has been the subject of considerable research. We also show how the method can be applied to a variant of the biased net models, which are not exponentially parameterized.
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