Machine learningNetwork science
贝叶斯随机图模型
贝叶斯随机图模型（Bayesian ERGM 或 BERGM）通过为模型参数设置先验分布并使用马尔可夫链蒙特卡洛方法获得完整的后验分布，扩展了经典的 ERGM 框架。该模型由 Caimo 和 Friel (2011) 提出，它允许研究人员在模拟社交网络和其他复杂网络的结构特征时量化参数不确定性并纳入先验知识。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55. DOI: 10.1016/j.socnet.2010.09.004 ↗
- Exponential random graph models. Wikipedia. link ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Exponential Random Graph Model (Bayesian ERGM). ScholarGate. https://scholargate.app/zh/network-analysis/bayesian-exponential-random-graph-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →