Model selection in random effects models for directed graphs using approximated Bayes factors

With the development of an MCMC algorithm, Bayesian model selection for the p2 model for directed graphs has become possible. This paper presents an empirical exploration in using approximate Bayes factors for model selection. For a social network of Dutch secondary school pupils from different ethn...

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Main Authors: Zijlstra, B, van Duijn, MAJ, Snijders, T
Format: Journal article
Sprog:English
Udgivet: 2005
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author Zijlstra, B
van Duijn, MAJ
Snijders, T
author_facet Zijlstra, B
van Duijn, MAJ
Snijders, T
author_sort Zijlstra, B
collection OXFORD
description With the development of an MCMC algorithm, Bayesian model selection for the p2 model for directed graphs has become possible. This paper presents an empirical exploration in using approximate Bayes factors for model selection. For a social network of Dutch secondary school pupils from different ethnic backgrounds it is investigated whether pupils report that they receive more emotional support from within their own ethnic group. Approximated Bayes factors seem to work, but considerable margins of error have to be reckoned with. © VVS, 2005.
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spelling oxford-uuid:b5610067-8c49-45e5-9a7e-08820930132b2022-03-27T04:32:58ZModel selection in random effects models for directed graphs using approximated Bayes factorsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b5610067-8c49-45e5-9a7e-08820930132bEnglishSymplectic Elements at Oxford2005Zijlstra, Bvan Duijn, MAJSnijders, TWith the development of an MCMC algorithm, Bayesian model selection for the p2 model for directed graphs has become possible. This paper presents an empirical exploration in using approximate Bayes factors for model selection. For a social network of Dutch secondary school pupils from different ethnic backgrounds it is investigated whether pupils report that they receive more emotional support from within their own ethnic group. Approximated Bayes factors seem to work, but considerable margins of error have to be reckoned with. © VVS, 2005.
spellingShingle Zijlstra, B
van Duijn, MAJ
Snijders, T
Model selection in random effects models for directed graphs using approximated Bayes factors
title Model selection in random effects models for directed graphs using approximated Bayes factors
title_full Model selection in random effects models for directed graphs using approximated Bayes factors
title_fullStr Model selection in random effects models for directed graphs using approximated Bayes factors
title_full_unstemmed Model selection in random effects models for directed graphs using approximated Bayes factors
title_short Model selection in random effects models for directed graphs using approximated Bayes factors
title_sort model selection in random effects models for directed graphs using approximated bayes factors
work_keys_str_mv AT zijlstrab modelselectioninrandomeffectsmodelsfordirectedgraphsusingapproximatedbayesfactors
AT vanduijnmaj modelselectioninrandomeffectsmodelsfordirectedgraphsusingapproximatedbayesfactors
AT snijderst modelselectioninrandomeffectsmodelsfordirectedgraphsusingapproximatedbayesfactors