Approximate Bayesian Computation for Exponential Random Graph Models for Large Social Networks

被引：11

作者：

Wang, Jing ^{[1
]}

Atchade, Yves F. ^{[2
]}

机构：

[1] Google, Mountain View, CA USA

[2] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2014年 / 43卷 / 02期

关键词：

Bayesian inference; Exponential random graph model; Intractable normalizing constants; Markov chain Monte Carlo; INTRACTABLE NORMALIZING CONSTANTS;

D O I：

10.1080/03610918.2012.703359

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We consider the issue of sampling from the posterior distribution of exponential random graph (ERG) models and other statistical models with intractable normalizing constants. Existing methods based on exact sampling are either infeasible or require very long computing time. We study a class of approximate Markov chain Monte Carlo (MCMC) sampling schemes that deal with this issue. We also develop a new Metropolis-Hastings kernel to sample sparse large networks from ERG models. We illustrate the proposed methods on several examples.

引用

页码：359 / 377

页数：19

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