Bayesian inference for exponential random graph models
Exponential random graph models are extremely difficult models to handle from a statistical viewpoint, since their normalising constant, which depends on model parameters, is available only in very trivial cases. We show how inference can be carried out in a Bayesian framework using a MCMC algorithm...
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| Veröffentlicht in: | Social networks 2011, Vol.33 (1), p.41-55 |
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| container_title | Social networks |
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| creator | Caimo, Alberto Friel, Nial |
| description | Exponential random graph models are extremely difficult models to handle from a statistical viewpoint, since their normalising constant, which depends on model parameters, is available only in very trivial cases. We show how inference can be carried out in a Bayesian framework using a MCMC algorithm, which circumvents the need to calculate the normalising constants. We use a population MCMC approach which accelerates convergence and improves mixing of the Markov chain. This approach improves performance with respect to the Monte Carlo maximum likelihood method of
Geyer and Thompson (1992). |
| doi_str_mv | 10.1016/j.socnet.2010.09.004 |
| format | Article |
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| ispartof | Social networks, 2011, Vol.33 (1), p.41-55 |
| issn | 0378-8733 1879-2111 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_870432619 |
| source | Elsevier ScienceDirect Journals; Sociological Abstracts |
| subjects | Algorithms Bayesian method Exponential random graph models History, theory and methodology Markov chain Monte Carlo Methodology Research methods Respect Social network analysis Social networks Sociology Statistics |
| title | Bayesian inference for exponential random graph models |
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