{"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/latent-bayesian-melding-for-integrating","title":"Latent Bayesian melding for integrating individual and population models","arxiv_id":"1510.09130","date":"2015-10-30","proceeding":"NeurIPS 2015 12","authors":["Mingjun Zhong","Nigel Goddard","Charles Sutton"],"abstract":"In many statistical problems, a more coarse-grained model may be suitable for\npopulation-level behaviour, whereas a more detailed model is appropriate for\naccurate modelling of individual behaviour. This raises the question of how to\nintegrate both types of models. Methods such as posterior regularization follow\nthe idea of generalized moment matching, in that they allow matching\nexpectations between two models, but sometimes both models are most\nconveniently expressed as latent variable models. We propose latent Bayesian\nmelding, which is motivated by averaging the distributions over populations\nstatistics of both the individual-level and the population-level models under a\nlogarithmic opinion pool framework. In a case study on electricity\ndisaggregation, which is a type of single-channel blind source separation\nproblem, we show that latent Bayesian melding leads to significantly more\naccurate predictions than an approach based solely on generalized moment\nmatching.","url_abs":"http://arxiv.org/abs/1510.09130v1","url_pdf":"http://arxiv.org/pdf/1510.09130v1.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":"latent-bayesian-melding-for-integrating","repo_url":"https://github.com/MingjunZhong/LatentBayesianMelding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"blind-source-separation","task_name":"blind source separation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}