Salīdzināt metodes

	Beiziešu temporālās tīklu analīzes	Beiziešu nejaušo grafu modelis (Bayesian Exponential Random Graph Model)
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2010s	2011
Autors	Hanneke, S.; Fu, W.; Xing, E. P. (among key contributors)	Caimo, A., & Friel, N.
Tips	Probabilistic generative model	Bayesian statistical model for networks
Pirmavots	Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.	Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55.
Citi nosaukumi	Bayesian dynamic network analysis, Bayesian time-varying network model, BTNA, Bayesian longitudinal network analysis	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM
Saistītās	4	4
Kopsavilkums	Bayesian temporal network analysis combines probabilistic Bayesian inference with time-ordered relational data to model how network structures evolve, quantify uncertainty around structural estimates, and make principled predictions about future connectivity patterns. It provides credible intervals on edge probabilities and community assignments rather than bare point estimates.	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.