{"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/mortality-forecasting-using-variational","title":"Mortality Forecasting using Variational Inference","arxiv_id":"2305.15943","date":"2023-05-25","proceeding":null,"authors":["Patrik Andersson","Mathias Lindholm"],"abstract":"This paper considers the problem of forecasting mortality rates. A large number of models have already been proposed for this task, but they generally have the disadvantage of either estimating the model in a two-step process, possibly losing efficiency, or relying on methods that are cumbersome for the practitioner to use. We instead propose using variational inference and the probabilistic programming library Pyro for estimating the model. This allows for flexibility in modelling assumptions while still being able to estimate the full model in one step. The models are fitted on Swedish mortality data and we find that the in-sample fit is good and that the forecasting performance is better than other popular models. Code is available at https://github.com/LPAndersson/VImortality.","url_abs":"https://arxiv.org/abs/2305.15943v1","url_pdf":"https://arxiv.org/pdf/2305.15943v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"mortality-forecasting-using-variational","repo_url":"https://github.com/lpandersson/vimortality","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}