指数族随机图模型的一种完美抽样方法

IF 1.3 4区社会学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Mathematical Sociology Pub Date : 2017-10-08 DOI:10.1080/0022250X.2017.1396985

C. Butts

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引用次数: 17

摘要

具有非平凡边缘依赖性的偏离随机图模型的生成是一个越来越重要的问题。在这里，我们介绍了一种方法，该方法允许从指数族形式的随机图模型（“指数族随机图”模型）中进行完美采样，使用来自过去的耦合的变体。我们通过在马尔可夫图中的应用来说明该方法的使用，马尔可夫图族一直是大量研究的主题。我们还展示了该方法如何应用于非指数参数化的有偏网络模型的变体。

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A perfect sampling method for exponential family random graph models

ABSTRACT Generation of deviates from random graph models with nontrivial 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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来源期刊

Journal of Mathematical Sociology 数学-数学跨学科应用

CiteScore

2.90

自引率

10.00%

发文量

审稿时长

>12 weeks

期刊介绍： The goal of the Journal of Mathematical Sociology is to publish models and mathematical techniques that would likely be useful to professional sociologists. The Journal also welcomes papers of mutual interest to social scientists and other social and behavioral scientists, as well as papers by non-social scientists that may encourage fruitful connections between sociology and other disciplines. Reviews of new or developing areas of mathematics and mathematical modeling that may have significant applications in sociology will also be considered. The Journal of Mathematical Sociology is published in association with the International Network for Social Network Analysis, the Japanese Association for Mathematical Sociology, the Mathematical Sociology Section of the American Sociological Association, and the Methodology Section of the American Sociological Association.