{"id":20603679,"url":"https://github.com/patrickm663/the-actuary-bnn-mortality-modelling","last_synced_at":"2025-07-20T15:33:28.469Z","repository":{"id":238369205,"uuid":"796410871","full_name":"patrickm663/the-actuary-BNN-mortality-modelling","owner":"patrickm663","description":"Some applications of Bayesian Neural Networks for submission to The Actuary Magazine.","archived":false,"fork":false,"pushed_at":"2024-07-05T13:28:25.000Z","size":7162,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-06T16:48:46.330Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/patrickm663.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-05-05T21:08:41.000Z","updated_at":"2024-08-06T12:26:00.000Z","dependencies_parsed_at":"2024-05-05T22:24:11.389Z","dependency_job_id":"87a7ed9f-b69b-4c63-bde9-6d31d6f839c5","html_url":"https://github.com/patrickm663/the-actuary-BNN-mortality-modelling","commit_stats":null,"previous_names":["patrickm663/the-actuary-bnn-mortality-modelling"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/patrickm663/the-actuary-BNN-mortality-modelling","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/patrickm663%2Fthe-actuary-BNN-mortality-modelling","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/patrickm663%2Fthe-actuary-BNN-mortality-modelling/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/patrickm663%2Fthe-actuary-BNN-mortality-modelling/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/patrickm663%2Fthe-actuary-BNN-mortality-modelling/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/patrickm663","download_url":"https://codeload.github.com/patrickm663/the-actuary-BNN-mortality-modelling/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/patrickm663%2Fthe-actuary-BNN-mortality-modelling/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266151525,"owners_count":23884436,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-16T09:18:08.720Z","updated_at":"2025-07-20T15:33:28.452Z","avatar_url":"https://github.com/patrickm663.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# BNN Mortality Modelling\nSome applications of Bayesian Neural Networks for submission to The Actuary Magazine.\n\n## Set-up\nThis notebook can be run as follows:\n1. Ensure Julia is installed\n2. Clone the repo and enter the directory\n3. Start the Julia REPL and instantiate the project (i.e. install the dependencies)\n```\njulia --project=.\n```\n```julia\njulia\u003e ] ## to enter Pkg mode\n(the-actuary-BNN-mortality-m...) pkg\u003e instantiate ## installs the dependencies\n```\n4. Exit Pkg mode using `Ctrl+C`\n5. While still in the REPL, launch Pluto\n```julia\njulia\u003e using Pluto\njulia\u003e Pluto.run()\n```\n6. Navigate to http://localhost:1234 in your browser (if not automatically redirected)\n7. Open `notebook/main.jl` from Pluto's user interface\n\n## Results\nGraphs can be viewed in `results/`. HTML output of the notebook can be viewed in `results/main.html`.\n\n## References\n[1] Bravo, J.M. 2021, Forecasting mortality rates with Recurrent Neural Networks: A preliminary investigation using Portuguese data. CAPSI 2021 Proceedings. 7. https://aisel.aisnet.org/capsi2021/7\n\n[2] Bennett, T.D., Russell, S., and Albers, D.J. 2021 Neural Networks for Mortality Prediction: Ready for Prime Time? Pediatr Crit Care Med. 2021 Jun 1;22(6):578-581. doi: 10.1097/PCC.0000000000002710. PMID: 34078844; PMCID: PMC8188609.\n\n[3] Schnürch, S. and Korn, R. 2022. Point and interval forecasts of death rates using neural networks, ASTIN Bulletin, 52(1):333–360.\n\n[4] Neal, R.M. 1994. Bayesian Learning for Neural Networks. Ph.D. thesis, University of Toronto, Canada.\n\n[5] World Health Organization. 2024. Life tables: Life tables by country South Africa. Available at: https://apps.who.int/gho/data/view.searo.61540?lang=en\n\n[6] Bishops, C.M. 1995. Neural networks for pattern recognition, chapter 10: Bayesian Techniques.  Department of Computer Science and Applied Mathematics, Aston University, Birmingham, UK.\n\n[7] Hoffman, M.D. and Gelman, A. 2011. The No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. arXiv preprint arXiv:1111.4246.\n\n[8] Neal, R. 1994. An improved acceptance procedure for the hybrid Monte Carlo algorithm. Journal of Computational Physics, 111:194–203.\n\n[9] Neal, R. 2011. Handbook of Markov Chain Monte Carlo. CRC Press.\n\n[10] Graves, A. 2011. Practical variational inference for neural networks. Department of Computer Science, University of Toronto, Canada.\n\n[11] Lee, R.D. and Carter, L.R. 1992. Modeling and Forecasting U.S. Mortality, Journal of the American Statistical Association, 87:419, 659-671\n\n[12] Ramachandran, P., Zoph, B., and Le, Q.V. 2017. Swish: a self-gated activation function, Google Brain. arXiv:1710.05941\n\n[13] Avik, P. 2023. Lux: Explicit Parameterization of Deep Neural Networks in Julia (v0.5.41). Zenodo, Available at:  https://github.com/LuxDL/Lux.jl\n\n[14] Ge, H., Xu, K., and Ghahramani, Z. 2018. Turing: a language for flexible probabilistic inference, International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 1682-1690. Available at: https://github.com/TuringLang/Turing.jl\n\n[15] Innes, M. 2019. Tracker.jl. Available at: https://github.com/FluxML/Tracker.jl\n\n[16] Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., and Bürkner, P. 2019. Rank-normalization, folding, and localization: an improved R-hat for assessing convergence of MCMC. arXiv preprint arXiv:1903.08008\n\n[17] Cloudenback, A. 2024. JuliaActuary. Available at: https://juliaactuary.org\n\n## License\nMIT licensed.\n\n## TODO\n- [ ] Clean-up notebook\n- [ ] Link to article once available\n- [ ] Add summary of results in README\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpatrickm663%2Fthe-actuary-bnn-mortality-modelling","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpatrickm663%2Fthe-actuary-bnn-mortality-modelling","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpatrickm663%2Fthe-actuary-bnn-mortality-modelling/lists"}