Comparer des méthodes

	Analyse Bayésienne des Réseaux d'Égo	Modèle bayésien de graphe aléatoire exponentiel
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2010s	2011
Auteur d'origine	Various (Bayesian SNA tradition; Krivitsky, Kolaczyk, Handcock among key contributors)	Caimo, A., & Friel, N.
Type	Probabilistic network model	Bayesian statistical model for networks
Source fondatrice	Krivitsky, P. N., & Kolaczyk, E. D. (2015). On the question of effective sample size in network modeling: An asymptotic inquiry. Statistical Science, 30(2), 184–198.	Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55.
Alias	Bayesian personal network analysis, Bayesian egocentric network analysis, probabilistic ego network modeling, Bayesian egonet	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM
Apparentées	5	4
Résumé	Bayesian ego network analysis applies probabilistic inference to ego-centered (personal) network data, combining a likelihood model for the ego's local network with prior distributions over network parameters. The result is a full posterior distribution that quantifies uncertainty about structural features such as alter composition, tie density, and network size — rather than producing point estimates alone.	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.