{"id":13655267,"url":"https://github.com/andymiller/vboost","last_synced_at":"2025-04-23T12:33:22.920Z","repository":{"id":143127287,"uuid":"93546878","full_name":"andymiller/vboost","owner":"andymiller","description":"code supplement for variational boosting (https://arxiv.org/abs/1611.06585)","archived":false,"fork":false,"pushed_at":"2017-07-24T17:03:53.000Z","size":11855,"stargazers_count":11,"open_issues_count":0,"forks_count":5,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-11-10T07:38:03.922Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","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/andymiller.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}},"created_at":"2017-06-06T17:45:54.000Z","updated_at":"2022-11-15T11:42:33.000Z","dependencies_parsed_at":"2023-03-30T06:50:51.404Z","dependency_job_id":null,"html_url":"https://github.com/andymiller/vboost","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andymiller%2Fvboost","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andymiller%2Fvboost/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andymiller%2Fvboost/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andymiller%2Fvboost/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/andymiller","download_url":"https://codeload.github.com/andymiller/vboost/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250435376,"owners_count":21430269,"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":"2024-08-02T03:01:00.754Z","updated_at":"2025-04-23T12:33:17.888Z","avatar_url":"https://github.com/andymiller.png","language":"Python","readme":"# vboost\n\ncode for [Variational Boosting: Iteratively Refining Posterior Approximations](https://arxiv.org/abs/1611.06585)\n\n### Abstract\n\n\u003e We propose a black-box variational inference method to approximate\n\u003e intractable distributions with an increasingly rich approximating class.\n\u003e Our method, termed variational boosting, iteratively refines an existing\n\u003e variational approximation by solving a sequence of optimization problems,\n\u003e allowing the practitioner to trade computation time for accuracy.\n\u003e We show how to expand the variational approximating class by incorporating\n\u003e additional covariance structure and by introducing new components to form a\n\u003e mixture. We apply variational boosting to synthetic and real statistical\n\u003e models, and show that resulting posterior inferences compare favorably to\n\u003e existing posterior approximation algorithms in both accuracy and efficiency.\n\nAuthors:\n[Andrew Miller](http://andymiller.github.io/),\n[Nick Foti](http://nfoti.github.io/), and\n[Ryan Adams](http://people.seas.harvard.edu/~rpa/).\n\n### Requires\n\n* [`autograd`](https://github.com/HIPS/autograd) + its requirements (`numpy`, etc).\n  Our code is compatible with [this `autograd` commit](https://github.com/HIPS/autograd/tree/42a57226442417785efe3bd5ba543b958680b765) or later.\n  You can install the master version with\n  `pip install git+git://github.com/HIPS/autograd.git@master`.\n* [`pyprind `](https://github.com/rasbt/pyprind)\n* [`sampyl`](https://github.com/mcleonard/sampyl) for MCMC experiments\n","funding_links":[],"categories":["2017"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandymiller%2Fvboost","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandymiller%2Fvboost","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandymiller%2Fvboost/lists"}