Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Bayesiaanse ego-netwerkanalyse× | Bayesiaans Exponentieel Willekeurig Graafmodel× | |
|---|---|---|
| Vakgebied | Netwerkanalyse | Netwerkanalyse |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2010s | 2011 |
| Grondlegger≠ | Various (Bayesian SNA tradition; Krivitsky, Kolaczyk, Handcock among key contributors) | Caimo, A., & Friel, N. |
| Type≠ | Probabilistic network model | Bayesian statistical model for networks |
| Oorspronkelijke bron≠ | 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. DOI ↗ | Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55. DOI ↗ |
| Aliassen | Bayesian personal network analysis, Bayesian egocentric network analysis, probabilistic ego network modeling, Bayesian egonet | Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM |
| Verwant≠ | 5 | 4 |
| Samenvatting≠ | 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. |
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