Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовский анализ эгосетей× | Bayesian Exponential Random Graph Model× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2010s | 2011 |
| Автор метода≠ | Various (Bayesian SNA tradition; Krivitsky, Kolaczyk, Handcock among key contributors) | Caimo, A., & Friel, N. |
| Тип≠ | Probabilistic network model | Bayesian statistical model for networks |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | Bayesian personal network analysis, Bayesian egocentric network analysis, probabilistic ego network modeling, Bayesian egonet | Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM |
| Связанные≠ | 5 | 4 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
|
|