Deterministic Bayesian inference for the p* model

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Title: Deterministic Bayesian inference for the p* model
Authors: Austad, Haakon
Friel, Nial
Permanent link: http://hdl.handle.net/10197/2446
Date: 2010
Abstract: The p* model is widely used in social network analysis. The likelihood of a network under this model is impossible to calculate for all but trivially small networks. Various approximation have been presented in the literature, and the pseudolikelihood approximation is the most popular. The aim of this paper is to introduce two likelihood approximations which have the pseudolikelihood estimator as a special case. We show, for the examples that we have considered, that both approximations result in improved estimation of model parameters with respect to the standard methodological approaches. We provide a deterministic approach and also illustrate how Bayesian model choice can be carried out in this setting.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Journal of Machine Learning Research (JMLR)
Keywords: Exponential random graph model;Composite likelihood
Subject LCSH: Social networks--Mathematical models
Random graphs
Bayesian statistical decision theory
Language: en
Status of Item: Peer reviewed
Is part of: JMLR Workshop and Conference Proceedings Volume 9 : AISTATS 2010 : Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics May 13-15, 2010
Conference Details: 13th International conference on Artificial Intelligence and Statistics (AISTATS, 2010), Chia Laguna Resort, Sardinia, Italy, May 13-15 2010
Appears in Collections:Mathematics and Statistics Research Collection

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