%0 Journal Article %1 plummer_penalized_2008 %A Plummer, Martyn %D 2008 %J Biostatistics %K Bayesian Carlo, DIC, JAGS/BUGS, Markov Monte \_tablet, approach, chain hierarchical inference, information mixture model model, selection theoretic %N 3 %P 523--539 %R 10.1093/biostatistics/kxm049 %T Penalized loss functions for Bayesian model comparison %U http://biostatistics.oxfordjournals.org/content/9/3/523 %V 9 %X The deviance information criterion (DIC) is widely used for Bayesian model comparison, despite the lack of a clear theoretical foundation. DIC is shown to be an approximation to a penalized loss function based on the deviance, with a penalty derived from a cross-validation argument. This approximation is valid only when the effective number of parameters in the model is much smaller than the number of independent observations. In disease mapping, a typical application of DIC, this assumption does not hold and DIC under-penalizes more complex models. Another deviance-based loss function, derived from the same decision-theoretic framework, is applied to mixture models, which have previously been considered an unsuitable application for DIC

@article{plummer_penalized_2008, abstract = {The deviance information criterion (DIC) is widely used for Bayesian model comparison, despite the lack of a clear theoretical foundation. DIC is shown to be an approximation to a penalized loss function based on the deviance, with a penalty derived from a cross-validation argument. This approximation is valid only when the effective number of parameters in the model is much smaller than the number of independent observations. In disease mapping, a typical application of DIC, this assumption does not hold and DIC under-penalizes more complex models. Another deviance-based loss function, derived from the same decision-theoretic framework, is applied to mixture models, which have previously been considered an unsuitable application for DIC}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Plummer, Martyn}, biburl = {https://www.bibsonomy.org/bibtex/2d4ed1f0a9bc016b102f979a592ec3d31/yourwelcome}, doi = {10.1093/biostatistics/kxm049}, interhash = {bd5e3b059a6917bd926e720630579c97}, intrahash = {d4ed1f0a9bc016b102f979a592ec3d31}, issn = {1465-4644, 1468-4357}, journal = {Biostatistics}, keywords = {Bayesian Carlo, DIC, JAGS/BUGS, Markov Monte \_tablet, approach, chain hierarchical inference, information mixture model model, selection theoretic}, language = {en}, month = jul, number = 3, pages = {523--539}, pmid = {18209015}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Penalized loss functions for {Bayesian} model comparison}, url = {http://biostatistics.oxfordjournals.org/content/9/3/523}, urldate = {2014-04-07}, volume = 9, year = 2008 }