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By using *Likelihood-Free Inference* (LFI) schemes, in particular *Approximate Bayesian Computation* (ABC), `pyLFI` estimates the posterior distributions over model parameters.\n\n## Overview\n\n`pyLFI` presently includes the following methods:\n\n* Rejection ABC\n* MCMC ABC\n* Post-sampling regression adjustment.\n\n`pyLFI` was created as a part of the author's [Master thesis](https://github.com/nicolossus/Master-thesis).\n\n## Installation instructions\n\n### Install with pip\n`pyLFI` can be installed directly from [PyPI](https://pypi.org/project/pylfi/):\n\n    $ pip install pylfi\n\n## Requirements\n* `Python` \u003e= 3.8\n\n## Documentation\nDocumentation can be found at [pylfi.readthedocs.io](https://pylfi.readthedocs.io/).\n\n## Getting started\nCheck out the [Examples gallery](https://pylfi.readthedocs.io/en/latest/auto_examples/index.html) in the documentation.\n\n## Automated build and test\nThe repository uses continuous integration (CI) workflows to build and test the project directly with GitHub Actions. Tests are provided in the [`tests`](tests) folder. Run tests locally with `pytest`:\n\n    $ python -m pytest tests -v\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnicolossus%2Fpylfi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnicolossus%2Fpylfi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnicolossus%2Fpylfi/lists"}