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	Bayesowski model losowego grafu wykładniczego	Bayesowski Model Bloków Stochastycznych
Dziedzina	Analiza sieci	Analiza sieci
Rodzina	Machine learning	Machine learning
Rok powstania	2011	2001–2014
Twórca	Caimo, A., & Friel, N.	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
Typ	Bayesian statistical model for networks	Probabilistic generative model with Bayesian inference
Źródło pierwotne	Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55.	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.
Inne nazwy	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model
Pokrewne	4	5
Podsumowanie	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.	The Bayesian Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches.