{"id":37085403,"url":"https://github.com/nicta/revrand","last_synced_at":"2026-01-14T10:29:59.647Z","repository":{"id":32218763,"uuid":"35792686","full_name":"NICTA/revrand","owner":"NICTA","description":"A library of scalable Bayesian generalised linear models with fancy features","archived":false,"fork":false,"pushed_at":"2017-09-07T04:29:27.000Z","size":86923,"stargazers_count":60,"open_issues_count":8,"forks_count":14,"subscribers_count":18,"default_branch":"master","last_synced_at":"2025-12-05T14:42:39.285Z","etag":null,"topics":["bayesian-inference","fourier-features","gaussian-processes","generalized-linear-models","regression"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/NICTA.png","metadata":{"files":{"readme":"README.rst","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}},"created_at":"2015-05-18T02:29:52.000Z","updated_at":"2024-12-11T03:13:52.000Z","dependencies_parsed_at":"2022-08-28T00:12:25.173Z","dependency_job_id":null,"html_url":"https://github.com/NICTA/revrand","commit_stats":null,"previous_names":[],"tags_count":28,"template":false,"template_full_name":null,"purl":"pkg:github/NICTA/revrand","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NICTA%2Frevrand","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NICTA%2Frevrand/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NICTA%2Frevrand/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NICTA%2Frevrand/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NICTA","download_url":"https://codeload.github.com/NICTA/revrand/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NICTA%2Frevrand/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28417318,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-14T10:25:19.714Z","status":"ssl_error","status_checked_at":"2026-01-14T10:22:49.371Z","response_time":107,"last_error":"SSL_read: 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","fourier-features","gaussian-processes","generalized-linear-models","regression"],"created_at":"2026-01-14T10:29:58.870Z","updated_at":"2026-01-14T10:29:59.639Z","avatar_url":"https://github.com/NICTA.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"=======\nrevrand \n=======\n\n.. image:: https://travis-ci.org/NICTA/revrand.svg?branch=master\n   :target: https://travis-ci.org/NICTA/revrand\n\n.. image:: https://codecov.io/github/NICTA/revrand/coverage.svg?branch=master\n    :target: https://codecov.io/github/NICTA/revrand?branch=master\n\n\n**Note**: we are not actively developing this library anymore, but we are still\nmaintaining it. We recommend instead looking at `Aboleth \n\u003chttps://github.com/data61/aboleth\u003e`_, which has similar functionality and is \nimplemented on top of TensorFlow.\n\n\n------------------------------------------------------------------------------\nA library of scalable Bayesian generalized linear models with *fancy* features\n------------------------------------------------------------------------------\n\n*revrand* is a python (2 and 3) **supervised machine learning** library that\ncontains implementations of various Bayesian linear and generalized linear\nmodels (i.e. Bayesian linear regression and Bayesian generalized linear\nregression). \n\n*revrand* can be used for **large scale approximate Gaussian process\nregression**, like `GPflow \u003chttps://github.com/GPflow/GPflow\u003e`_ and `GPy\n\u003chttps://github.com/SheffieldML/GPy\u003e`_, but it uses random basis kernel\napproximations (see [1]_, [2]_, [3]_) as opposed to inducing point\napproximations.\n\nA few features of this library are:\n\n- Random Basis functions that can be used to approximate Gaussian processes\n  with shift invariant covariance functions (e.g. Matern) when used with linear\n  models [1]_, [2]_, [3]_.\n- A fancy basis functions/feature composition framework for combining basis\n  functions like those above and radial basis functions, sigmoidal basis\n  functions, polynomial basis functions etc *with basis function parameter\n  learning*.\n- Non-Gaussian likelihoods with Bayesian generalized linear models (GLMs). We\n  infer all of the parameters in the GLMs using stochastic variational \n  inference [4]_, and we approximate the posterior over the weights with a\n  mixture of Gaussians, like [5]_.\n- Large scale learning using stochastic gradients (Adam, AdaDelta and more).\n- Scikit Learn compatibility, i.e. usable with `pipelines\n  \u003chttp://scikit-learn.org/stable/modules/pipeline.html\u003e`_.\n- A host of decorators for `scipy.optimize.minimize\n  \u003chttps://docs.scipy.org/doc/scipy/reference/optimize.html\u003e`_ and stochastic \n  gradients that enhance the functionality of these optimisers.\n\nHere is an example of approximating a Matern 3/2 kernel with some of our basis\nfunctions,\n\n.. image:: docs/matern32.png\n\nhere is an example of the algorithms in *revrand* approximating a Gaussian\nProcess,\n\n.. image:: docs/glm_sgd_demo.png\n\nand here is an example of running using our Bayesian GLM with a Poisson\nlikelihood and integer observations,\n\n.. image:: docs/glm_demo.png\n\nHave a look at some of the demo `notebooks \u003cdemos/\u003e`_ for how we generated\nthese plots, and more!\n\nQuickstart\n----------\n\nTo install, you can use ``pip``:\n\n.. code:: console\n\n   $ pip install revrand\n\nor simply run ``setup.py`` in the location where you have cloned or\ndownloaded this repository:\n\n.. code:: console\n\n   $ python setup.py install\n\nNow have a look at our `quickstart guide\n\u003chttp://nicta.github.io/revrand/quickstart.html\u003e`_ to get up and running\nquickly!\n\n\nUseful Links\n------------\n\nHome Page\n    http://github.com/nicta/revrand\n\nDocumentation\n    http://nicta.github.io/revrand\n\nReport on the algorithms in *revrand*\n    https://github.com/NICTA/revrand/blob/master/docs/report/report.pdf\n\nIssue tracking\n    https://github.com/nicta/revrand/issues\n\n\nBugs \u0026 Feedback\n---------------\n\nFor bugs, questions and discussions, please use \n`Github Issues \u003chttps://github.com/NICTA/revrand/issues\u003e`_.\n\n\nAuthors\n-------\n\n- `Daniel Steinberg \u003chttps://github.com/dsteinberg\u003e`_\n- `Louis Tiao \u003chttps://github.com/ltiao\u003e`_\n- `Alistair Reid \u003chttps://github.com/AlistaiReid\u003e`_\n- `Lachlan McCalman \u003chttps://github.com/lmccalman\u003e`_\n- `Simon O'Callaghan \u003chttps://github.com/socallaghan\u003e`_\n\n\nReferences\n----------\n\n.. [1] Yang, Z., Smola, A. J., Song, L., \u0026 Wilson, A. G. \"A la Carte --\n   Learning Fast Kernels\". Proceedings of the Eighteenth International\n   Conference on Artificial Intelligence and Statistics, pp. 1098-1106,\n   2015.\n.. [2] Le, Q., Sarlos, T., \u0026 Smola, A. \"Fastfood-approximating kernel\n   expansions in loglinear time.\" Proceedings of the international conference\n   on machine learning. 2013.\n.. [3] Rahimi, A., \u0026 Recht, B. \"Random features for large-scale kernel\n   machines\". Advances in neural information processing systems. 2007. \n.. [4] Kingma, D. P., \u0026 Welling, M. \"Auto-encoding variational Bayes\".\n   Proceedings of the 2nd International Conference on Learning Representations\n   (ICLR). 2014.\n.. [5] Gershman, S., Hoffman, M., \u0026 Blei, D. \"Nonparametric variational\n   inference\". Proceedings of the international conference on machine learning.\n   2012.\n\n\nCopyright \u0026 License\n-------------------\n\nCopyright 2015 National ICT Australia.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnicta%2Frevrand","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnicta%2Frevrand","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnicta%2Frevrand/lists"}