方法证据记录
Bayesian Exponential Random Graph Model
The Bayesian Exponential Random Graph Model (Bayesian ERGM or BERGM) extends the classical ERGM framework by placing prior distributions over the model parameters and using Markov chain Monte Carlo methods to obtain full posterior distributions. Introduced by Caimo and Friel (2011), it allows researchers to quantify parameter uncertainty and incorporate prior knowledge when modelling the structural features of social and other complex networks.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Exponential Random Graph Model (Bayesian ERGM)
分类方法记录 · ml-model / network-analysis
- 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. · URL
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