{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/graphical-posterior-predictive-classifier","title":"Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs","arxiv_id":"1707.06792","date":"2017-07-21","proceeding":null,"authors":["Tatjana Pavlenko","Felix Leopoldo Rios"],"abstract":"In this study, we present a multi-class graphical Bayesian predictive\nclassifier that incorporates the uncertainty in the model selection into the\nstandard Bayesian formalism. For each class, the dependence structure\nunderlying the observed features is represented by a set of decomposable\nGaussian graphical models. Emphasis is then placed on the Bayesian model\naveraging which takes full account of the class-specific model uncertainty by\naveraging over the posterior graph model probabilities. An explicit evaluation\nof the model probabilities is well known to be infeasible. To address this\nissue, we consider the particle Gibbs strategy of Olsson et al. (2018b) for\nposterior sampling from decomposable graphical models which utilizes the\nChristmas tree algorithm of Olsson et al. (2018a) as proposal kernel. We also\nderive a strong hyper Markov law which we call the hyper normal Wishart law\nthat allow to perform the resultant Bayesian calculations locally. The proposed\npredictive graphical classifier reveals superior performance compared to the\nordinary Bayesian predictive rule that does not account for the model\nuncertainty, as well as to a number of out-of-the-box classifiers.","url_abs":"http://arxiv.org/abs/1707.06792v4","url_pdf":"http://arxiv.org/pdf/1707.06792v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"graphical-posterior-predictive-classifier","repo_url":"https://github.com/felixleopoldo/trilearn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}