Deterministic Bayesian inference for the $p*$ model

Haakon Austad, Nial Friel
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:41-48, 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v9-austad10a, title = {Deterministic Bayesian inference for the p* model}, author = {Austad, Haakon and Friel, Nial}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {41--48}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/austad10a/austad10a.pdf}, url = {https://proceedings.mlr.press/v9/austad10a.html}, 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.} }
Endnote
%0 Conference Paper %T Deterministic Bayesian inference for the $p*$ model %A Haakon Austad %A Nial Friel %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-austad10a %I PMLR %P 41--48 %U https://proceedings.mlr.press/v9/austad10a.html %V 9 %X 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.
RIS
TY - CPAPER TI - Deterministic Bayesian inference for the $p*$ model AU - Haakon Austad AU - Nial Friel BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-austad10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 41 EP - 48 L1 - http://proceedings.mlr.press/v9/austad10a/austad10a.pdf UR - https://proceedings.mlr.press/v9/austad10a.html AB - 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. ER -
APA
Austad, H. & Friel, N.. (2010). Deterministic Bayesian inference for the $p*$ model. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:41-48 Available from https://proceedings.mlr.press/v9/austad10a.html.

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