Salīdzināt metodes

	Beiziešu nejaušo grafu modelis (Bayesian Exponential Random Graph Model)	Stohastiskais bloku modelis
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2011	1983
Autors	Caimo, A., & Friel, N.	—
Tips	Bayesian statistical model for networks	Probabilistic generative graph model
Pirmavots	Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Citi nosaukumi	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Saistītās	4	7
Kopsavilkums	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 Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.