{"id":13699450,"url":"https://github.com/elfi-dev/elfi","last_synced_at":"2026-02-23T05:01:47.741Z","repository":{"id":13936596,"uuid":"69855441","full_name":"elfi-dev/elfi","owner":"elfi-dev","description":"ELFI - Engine for Likelihood-Free Inference","archived":false,"fork":false,"pushed_at":"2025-05-07T09:22:58.000Z","size":2253,"stargazers_count":279,"open_issues_count":15,"forks_count":62,"subscribers_count":18,"default_branch":"dev","last_synced_at":"2026-01-11T15:59:22.929Z","etag":null,"topics":["bayesian","bayesian-inference","inference","likelihood-free","python","simulator","statistics"],"latest_commit_sha":null,"homepage":"http://elfi.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/elfi-dev.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.rst","contributing":"CONTRIBUTING.rst","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":"2016-10-03T09:03:38.000Z","updated_at":"2025-12-08T00:38:55.000Z","dependencies_parsed_at":"2024-03-24T18:23:12.395Z","dependency_job_id":"a1035ba1-73c8-424f-8ef4-e89b4706a11a","html_url":"https://github.com/elfi-dev/elfi","commit_stats":{"total_commits":632,"total_committers":26,"mean_commits":"24.307692307692307","dds":0.7167721518987342,"last_synced_commit":"68a7418a26509ee6f2c36a3927f76b517eaf9c24"},"previous_names":[],"tags_count":24,"template":false,"template_full_name":null,"purl":"pkg:github/elfi-dev/elfi","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/elfi-dev%2Felfi","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/elfi-dev%2Felfi/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/elfi-dev%2Felfi/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/elfi-dev%2Felfi/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/elfi-dev","download_url":"https://codeload.github.com/elfi-dev/elfi/tar.gz/refs/heads/dev","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/elfi-dev%2Felfi/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29738083,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-23T04:51:08.365Z","status":"ssl_error","status_checked_at":"2026-02-23T04:49:15.865Z","response_time":90,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 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","bayesian-inference","inference","likelihood-free","python","simulator","statistics"],"created_at":"2024-08-02T20:00:33.516Z","updated_at":"2026-02-23T05:01:47.719Z","avatar_url":"https://github.com/elfi-dev.png","language":"Python","readme":"**Version 0.8.7 released!** See the [CHANGELOG](CHANGELOG.rst) and [notebooks](https://github.com/elfi-dev/notebooks).\n\n\u003cimg src=\"https://raw.githubusercontent.com/elfi-dev/elfi/dev/docs/logos/elfi_logo_text_nobg.png\" width=\"200\" /\u003e\n\nELFI - Engine for Likelihood-Free Inference\n===========================================\n\n[![Build Status](https://github.com/elfi-dev/elfi/actions/workflows/pytest.yml/badge.svg)](https://github.com/elfi-devs/elfi/actions)\n[![Documentation Status](https://readthedocs.org/projects/elfi/badge/?version=latest)](http://elfi.readthedocs.io/en/latest/?badge=latest)\n[![Gitter](https://badges.gitter.im/elfi-dev/elfi.svg)](https://gitter.im/elfi-dev/elfi?utm_source=badge\u0026utm_medium=badge\u0026utm_campaign=pr-badge)\n[![DOI](https://zenodo.org/badge/69855441.svg)](https://zenodo.org/badge/latestdoi/69855441)\n\n\u003cimg src=\"https://cloud.githubusercontent.com/assets/1233418/20178983/6e22ee44-a75c-11e6-8345-5934b55b9dc6.png\" width=\"15%\" align=\"right\"\u003e\u003c/img\u003e\n\nELFI is a statistical software package written in Python for likelihood-free inference (LFI) such as Approximate\nBayesian Computation ([ABC](https://en.wikipedia.org/wiki/Approximate_Bayesian_computation)).\nThe term LFI refers to a family of inference methods that replace the use of the likelihood function with a data\ngenerating simulator function. ELFI features an easy to use generative modeling syntax and supports parallelized\ninference out of the box.\n\nCurrently implemented LFI methods:\n- ABC Rejection sampler\n- Sequential Monte Carlo ABC sampler\n- SMC-ABC sampler with [adaptive threshold selection](https://projecteuclid.org/journals/bayesian-analysis/advance-publication/Adaptive-Approximate-Bayesian-Computation-Tolerance-Selection/10.1214/20-BA1211.full)\n- SMC-ABC sampler with [adaptive distance](https://projecteuclid.org/euclid.ba/1460641065)\n- [Bayesian Optimization for Likelihood-Free Inference (BOLFI)](http://jmlr.csail.mit.edu/papers/v17/15-017.html)\n- [Robust Optimisation Monte Carlo (ROMC)](https://arxiv.org/abs/1904.00670)\n- [Bayesian Optimization for Likelihood-Free Inference by Ratio Estimation (BOLFIRE)](https://helda.helsinki.fi/handle/10138/305039)\n- [Bayesian Synthetic Likelihood (BSL)](https://doi.org/10.1080/10618600.2017.1302882)\n\nOther notable included algorithms and methods:\n- Bayesian Optimization\n- [No-U-Turn-Sampler](http://jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdf), a Hamiltonian Monte Carlo MCMC sampler\n\nELFI also integrates tools for visualization, model comparison, diagnostics and post-processing.\n\nSee examples under [notebooks](https://github.com/elfi-dev/notebooks) to get started. Full\ndocumentation can be found at http://elfi.readthedocs.io/. Limited user-support may be\nasked from elfi-support.at.hiit.fi, but the\n[Gitter chat](https://gitter.im/elfi-dev/elfi?utm_source=share-link\u0026utm_medium=link\u0026utm_campaign=share-link)\nis preferable.\n\n\nInstallation with pip\n---------------------\n\nELFI requires Python 3.9 or greater. You can install ELFI by typing in your terminal:\n\n```\npip install elfi\n```\nor on some platforms using Python 3 specific syntax:\n```\npip3 install elfi\n```\n\nNote that in some environments you may need to first install `numpy` with\n`pip install numpy`. This is due to our dependency to `GPy` that uses `numpy` in its installation.\n\nInstallation from conda-forge\n-----------------------------\n\nInstalling `elfi` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:\n\n```\nconda config --add channels conda-forge\n```\n\nOnce the `conda-forge` channel has been enabled, `elfi` can be installed with:\n\n```\nconda install elfi\n```\n\nIt is possible to list all of the versions of `elfi` available on your platform with:\n\n```\nconda search elfi --channel conda-forge\n```\n\n### Optional dependencies\n\n- `graphviz` for drawing graphical models (needs [Graphviz](http://www.graphviz.org)), highly recommended\n\n\n### Installing Python 3\n\nIf you are new to Python, perhaps the simplest way to install a specific version of Python\nis with [Anaconda](https://www.continuum.io/downloads).\n\n### Virtual environment using Anaconda\n\nIt is very practical to create a virtual Python environment. This way you won't interfere\nwith your default Python environment and can easily use different versions of Python\nin different projects. You can create a virtual environment for ELFI using anaconda with:\n\n```\nconda create -n elfi python=3.9 numpy\nsource activate elfi\npip install elfi\n```\n\n### Docker container\n\nA simple Dockerfile with Jupyter support is also provided. This is especially suitable for running tests. Please see [Docker documentation](https://docs.docker.com/) for details.\n\n```\ngit clone --depth 1 https://github.com/elfi-dev/elfi.git\ncd elfi\nmake docker-build  # builds the image with requirements for dev\nmake docker  # runs a container with live elfi directory\n```\n\nTo open a Jupyter notebook, run\n```\njupyter notebook --ip 0.0.0.0 --no-browser --allow-root\n```\nwithin the container and then on host open the page http://localhost:8888. \n\n### Potential problems with installation\n\nELFI depends on several other Python packages, which have their own dependencies.\nResolving these may sometimes go wrong:\n- If you receive an error about missing `numpy`, please install it first.\n- If you receive an error about `yaml.load`, install `pyyaml`.\n- On OS X with Anaconda virtual environment say `conda install python.app` and then use\n`pythonw` instead of `python`.\n- Note that ELFI requires Python 3.9 or greater so try `pip3 install elfi`.\n- Make sure your Python installation meets the versions listed in `requirements.txt`.\n\n\nCitation\n--------\n\nIf you wish to cite ELFI, please use the paper in [JMLR](http://www.jmlr.org/papers/v19/17-374.html):\n\n```\n@article{JMLR:v19:17-374,\n  author  = {Jarno Lintusaari and Henri Vuollekoski and Antti Kangasr{\\\"a}{\\\"a}si{\\\"o} and Kusti Skyt{\\'e}n and Marko J{\\\"a}rvenp{\\\"a}{\\\"a} and Pekka Marttinen and Michael U. Gutmann and Aki Vehtari and Jukka Corander and Samuel Kaski},\n  title   = {ELFI: Engine for Likelihood-Free Inference},\n  journal = {Journal of Machine Learning Research},\n  year    = {2018},\n  volume  = {19},\n  number  = {16},\n  pages   = {1-7},\n  url     = {http://jmlr.org/papers/v19/17-374.html}\n}\n```\n","funding_links":[],"categories":["\u003cspan id=\"head30\"\u003e3.4. Bayesian Inference\u003c/span\u003e","Python"],"sub_categories":["\u003cspan id=\"head32\"\u003e3.4.2. Approximate Bayesian Computation (ABC)\u003c/span\u003e"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felfi-dev%2Felfi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Felfi-dev%2Felfi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felfi-dev%2Felfi/lists"}