کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1129387 955251 2011 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian inference for exponential random graph models
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آمار و احتمال
پیش نمایش صفحه اول مقاله
Bayesian inference for exponential random graph models
چکیده انگلیسی

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).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Social Networks - Volume 33, Issue 1, January 2011, Pages 41–55
نویسندگان
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