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Author and presenter Friel, Nial; School of Mathematical Sciences, University College Dublin Title Bayesian inference for the exponential random graph model Abstract This talk will present a new approach to carry out inference for the exponential random graph model. The exponential random graph is very widely used in the analysis of social networks, yet from a statistical view point it presents many difficulties, mostly notably because the likelihood cannot be evaluated for reasonably sized networks. The approach which we describe here, sidesteps these difficulties to a certain extent. The algorithm which we use to perform Bayesian inference is based on a Markov chain Monte Carlo algorithm and is therefore simulation-based -- it relies on the ability to able to draw networks exactly from the likelihood model. We outline how this algorithm can be extended to also yield estimates of the marginal likelihood or model evidence, thereby allowing one to make probability statements about the uncertainty of the statistical model itself. We will present several examples of how this methodology performs, and will also outline how it may be used using the statistical software R.
The ASA Spring Methodology Conference Organized in Europe by the Department of Methodology and Statistics at Tilburg University, the Netherlands. SESSION: Bayesian Methods SUMMARY Often researchers have an expectation about the ordering of the model parameters. This can be directly evaluated using Bayesian statistics. In this, one uses prior knowledge with respect to the model parameters. There are several ways of specifying the prior. The first speaker will go into detail in specifying reasonable and relevant priors. Then, Bayesian hypothesis testing is addressed. Subsequently, Bayesian factor analysis is discussed. Finally, a demonstration of combining evidence regarding multiple studies is given. Presenters: Rebecca Kuiper (chair of the session) r.m.kuiper@uu.nl Dept. Methodology & Statistics, Utrecht University Ruud Wetzels R.M.Wetzels@uva.nl University of Amsterdam, the Netherlands Carel Peeters C.F.W.Peeters@uu.nl Dept. Methodology & Statistics, Utrecht University Floryt van Wesel F.vanWesel@uu.nl Dept. Methodology & Statistics, Utrecht University
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The ASA Spring Methodology Conferen
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Friendships under study were formed
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the interplay of social background
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greatly inefficient. Only very few
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Data We use matched employer-employ
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KEY NOTE SPEAKER Snijders, Tom A.B.
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Presenter Hagenaars, Jacques A.P.;
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Presenter Croon, Marcel; Tilburg Sc
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Author and presenter Kuijpers, Rens
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Page 43 and 44: Presenter Engel, Uwe; Dept. of Soci
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Page 63 and 64: efore retirement. We identify four
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