{"id":21823259,"url":"https://github.com/lawmurray/birch","last_synced_at":"2025-04-05T07:03:14.963Z","repository":{"id":40598678,"uuid":"77641570","full_name":"lawmurray/Birch","owner":"lawmurray","description":"A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.","archived":false,"fork":false,"pushed_at":"2025-01-31T14:24:05.000Z","size":13812,"stargazers_count":115,"open_issues_count":3,"forks_count":14,"subscribers_count":14,"default_branch":"master","last_synced_at":"2025-03-29T06:05:50.356Z","etag":null,"topics":["autodiff","bayesian","bayesian-inference","bayesian-methods","bayesian-statistics","data-science","machine-learning","machine-learning-algorithms","machine-learning-projects","monte-carlo-methods","monte-carlo-sampling","probabilistic-programming-languages","statistics"],"latest_commit_sha":null,"homepage":"https://birch-lang.org","language":"C++","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/lawmurray.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","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":"2016-12-29T21:43:07.000Z","updated_at":"2025-03-17T07:02:02.000Z","dependencies_parsed_at":"2024-06-20T10:48:23.514Z","dependency_job_id":"bd2375b2-ccb6-474d-82dd-cdea75c8809c","html_url":"https://github.com/lawmurray/Birch","commit_stats":null,"previous_names":[],"tags_count":11,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lawmurray%2FBirch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lawmurray%2FBirch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lawmurray%2FBirch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lawmurray%2FBirch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lawmurray","download_url":"https://codeload.github.com/lawmurray/Birch/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247299831,"owners_count":20916190,"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":["autodiff","bayesian","bayesian-inference","bayesian-methods","bayesian-statistics","data-science","machine-learning","machine-learning-algorithms","machine-learning-projects","monte-carlo-methods","monte-carlo-sampling","probabilistic-programming-languages","statistics"],"created_at":"2024-11-27T17:29:43.246Z","updated_at":"2025-04-05T07:03:14.891Z","avatar_url":"https://github.com/lawmurray.png","language":"C++","readme":"# Birch\n\nBirch is a probabilistic programming language featuring automatic\nmarginalization, automatic conditioning, automatic differentiation, and\ninference algorithms based on Sequential Monte Carlo (SMC). The Birch language\ntranspiles to C++.\n\nSee [https://birch-lang.org](https://birch-lang.org) for a gentle introduction, and\n[https://docs.birch-lang.org](https://docs.birch-lang.org) for reference documentation.\n\n[![lawmurray](https://circleci.com/gh/lawmurray/Birch.svg?style=shield)](https://circleci.com/gh/lawmurray/Birch) [![codecov](https://codecov.io/gh/lawmurray/Birch/graph/badge.svg)](https://codecov.io/gh/lawmurray/Birch)\n\n\n## License\n\nBirch is open source software. It is licensed under the Apache License,\nVersion 2.0 (the \"License\"); you may not use it except in compliance with the\nLicense. You may obtain a copy of the License at\n\u003chttp://www.apache.org/licenses/LICENSE-2.0\u003e.\n\n\n## Getting started\n\nBinary packages may be available for your system, see [the website](https://birch-lang.org/getting-started/). If not, or if you have special requirements, you can install Birch from source. This requires:\n\n  * GNU autoconf, automake, libtool, flex, and bison\n  * [LibYAML](https://pyyaml.org/wiki/LibYAML)\n  * [Eigen](https://eigen.tuxfamily.org)\n\nThe following is optional but recommended for significant performance\nimprovements, and will be linked in automatically if found:\n\n  * [jemalloc](http://jemalloc.net/)\n\nAll Birch sources are in the same repository. Clone it:\n\n    git clone https://github.com/lawmurray/Birch.git\n\nand change to the `Birch` directory:\n\n    cd Birch\n\nThen proceed as follows. Note special instructions for Mac in step 2. In\naddition, on Mac, you can typically omit `sudo` from these commands.\n\n1. Install MemBirch by running, from within the `membirch/` directory:\n   ```\n   ./bootstrap\n   ./configure\n   make\n   sudo make install\n   ```\n\n2. Install NumBirch by running, from within the `numbirch/` directory:\n   ```\n   ./bootstrap\n   ./configure\n   make\n   sudo make install\n   ```\n\n3. Install Birch by running, from within the `birch/` directory:\n   ```\n   ./bootstrap\n   ./configure\n   make\n   sudo make install\n   ```\n\n4. Install the Birch standard library by running, from within the\n   `libraries/Standard/` directory:\n   ```\n   birch build\n   sudo birch install\n   ```\n\nThis constitutes a basic install. You can inspect the different components for\nadvanced options, such as disabling assertions to improve performance, or\nbuilding the CUDA backend for NumBirch. You may also like to install other\npackages in the `libraries/` directory. It is not usual to install the\npackages in the `examples/` directory, although you may like to build and run\nthese locally for learning purposes.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flawmurray%2Fbirch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flawmurray%2Fbirch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flawmurray%2Fbirch/lists"}