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Bayesian Exponential Random Graph Model/Evidence
Method evidence record

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.

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Bayesian Exponential Random Graph Model (Bayesian ERGM)
Taxonomic method record · 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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Related methods

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Taxonomic bucketBayesian Social Network Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Stochastic Block Modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketModularity Analysismachine-suggested · Relational suggestion, not evidence.Used in the same domainStochastic Block Modelmachine-suggested · Relational suggestion, not evidence.

Evidence status

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Sources

2 recorded citations, copied from the method source record.

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