{"id":20608906,"url":"https://github.com/jose-jaen/bayesian-statistics","last_synced_at":"2026-04-16T21:34:36.417Z","repository":{"id":148432142,"uuid":"545434362","full_name":"jose-jaen/Bayesian-Statistics","owner":"jose-jaen","description":null,"archived":false,"fork":false,"pushed_at":"2023-01-30T20:26:24.000Z","size":1013,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-06T17:49:22.074Z","etag":null,"topics":["bayesian-inference","bayesian-statistics","machine-learning","python","r","statistics"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jose-jaen.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2022-10-04T11:13:31.000Z","updated_at":"2023-01-31T15:09:50.000Z","dependencies_parsed_at":"2023-06-10T08:45:31.769Z","dependency_job_id":null,"html_url":"https://github.com/jose-jaen/Bayesian-Statistics","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jose-jaen/Bayesian-Statistics","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jose-jaen%2FBayesian-Statistics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jose-jaen%2FBayesian-Statistics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jose-jaen%2FBayesian-Statistics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jose-jaen%2FBayesian-Statistics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jose-jaen","download_url":"https://codeload.github.com/jose-jaen/Bayesian-Statistics/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jose-jaen%2FBayesian-Statistics/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31905787,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-16T18:22:33.417Z","status":"ssl_error","status_checked_at":"2026-04-16T18:21:47.142Z","response_time":69,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["bayesian-inference","bayesian-statistics","machine-learning","python","r","statistics"],"created_at":"2024-11-16T10:12:11.505Z","updated_at":"2026-04-16T21:34:36.389Z","avatar_url":"https://github.com/jose-jaen.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Bayesian Statistics\n\nImplementing Bayesian Inference to analytical problems.\n\n# US Presidential Candidate Prediction\n\nUsing tweets, we predict whether Trump or Clinton wrote them.\nNLP was used as to prepare unstructured data for modeling. We compare how a Naive Bayes classifier performs using Frequentist Statistics and Bayesian Statistics (Laplace Smoothing). We also employ a new algorithm for text analytics: TF-IDF.\n\n- [Paper](https://github.com/jose-jaen/Bayesian-Statistics/blob/main/Twitter%20US%20Candidate%20Prediction/Bayesian_Statistics__Tweet_Filter.pdf)\n- [R Code](https://github.com/jose-jaen/Bayesian-Statistics/blob/main/Twitter%20US%20Candidate%20Prediction/NLP_Tweets.r)\n- [Python Code](https://github.com/jose-jaen/Bayesian-Statistics/blob/main/Twitter%20US%20Candidate%20Prediction/NLP_tweets.py)\n\n# Conjugate priors simulation\n\nGiven a Gamma prior and exponentially distributed data points, we derive the marginal and predictive distribution of the data.\nWe also propose a mixture framework for combining prior beliefs.\n\n- [Paper](https://github.com/jose-jaen/Bayesian-Statistics/blob/main/Bayesian%20Conjugate%20Priors/Bayesian_Statistics__Conjugate_Prior.pdf)\n- [R Code](https://github.com/jose-jaen/Bayesian-Statistics/blob/main/Bayesian%20Conjugate%20Priors/Conjugate_priors.r)\n\n# Predicting Heart Disease\n\nA Generalized Linear Model, concretely, a logistic regression, is estimated to predict whether a patient had a heart disease or not.\nFeature selection is carried out with L1 Regularization or LASSO regression and then Frequentist and Bayesian Inference are compared.\n\n- [Paper](https://github.com/jose-jaen/Bayesian-Statistics/blob/main/Generalized%20Linear%20Models/Bayesian_Statistics__Regression.pdf)\n- [R Code](https://github.com/jose-jaen/Bayesian-Statistics/blob/main/Generalized%20Linear%20Models/Bayesian_GLS.r)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjose-jaen%2Fbayesian-statistics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjose-jaen%2Fbayesian-statistics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjose-jaen%2Fbayesian-statistics/lists"}