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https://github.com/elfi-dev/elfi

ELFI - Engine for Likelihood-Free Inference
https://github.com/elfi-dev/elfi

bayesian bayesian-inference inference likelihood-free python simulator statistics

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ELFI - Engine for Likelihood-Free Inference

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README 

        
**Version 0.8.7 released!** See the [CHANGELOG](CHANGELOG.rst) and [notebooks](https://github.com/elfi-dev/notebooks).






ELFI - Engine for Likelihood-Free Inference

===========================================



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[![Documentation Status](https://readthedocs.org/projects/elfi/badge/?version=latest)](http://elfi.readthedocs.io/en/latest/?badge=latest)

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[![DOI](https://zenodo.org/badge/69855441.svg)](https://zenodo.org/badge/latestdoi/69855441)






ELFI is a statistical software package written in Python for likelihood-free inference (LFI) such as Approximate

Bayesian Computation ([ABC](https://en.wikipedia.org/wiki/Approximate_Bayesian_computation)).

The term LFI refers to a family of inference methods that replace the use of the likelihood function with a data

generating simulator function. ELFI features an easy to use generative modeling syntax and supports parallelized

inference out of the box.



Currently implemented LFI methods:

- ABC Rejection sampler

- Sequential Monte Carlo ABC sampler

- 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)

- SMC-ABC sampler with [adaptive distance](https://projecteuclid.org/euclid.ba/1460641065)

- [Bayesian Optimization for Likelihood-Free Inference (BOLFI)](http://jmlr.csail.mit.edu/papers/v17/15-017.html)

- [Robust Optimisation Monte Carlo (ROMC)](https://arxiv.org/abs/1904.00670)

- [Bayesian Optimization for Likelihood-Free Inference by Ratio Estimation (BOLFIRE)](https://helda.helsinki.fi/handle/10138/305039)

- [Bayesian Synthetic Likelihood (BSL)](https://doi.org/10.1080/10618600.2017.1302882)



Other notable included algorithms and methods:

- Bayesian Optimization

- [No-U-Turn-Sampler](http://jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdf), a Hamiltonian Monte Carlo MCMC sampler



ELFI also integrates tools for visualization, model comparison, diagnostics and post-processing.



See examples under [notebooks](https://github.com/elfi-dev/notebooks) to get started. Full

documentation can be found at http://elfi.readthedocs.io/. Limited user-support may be

asked from elfi-support.at.hiit.fi, but the

[Gitter chat](https://gitter.im/elfi-dev/elfi?utm_source=share-link&utm_medium=link&utm_campaign=share-link)

is preferable.



Installation with pip

---------------------



ELFI requires Python 3.9 or greater. You can install ELFI by typing in your terminal:



```

pip install elfi

```

or on some platforms using Python 3 specific syntax:

```

pip3 install elfi

```



Note that in some environments you may need to first install `numpy` with

`pip install numpy`. This is due to our dependency to `GPy` that uses `numpy` in its installation.



Installation from conda-forge

-----------------------------



Installing `elfi` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:



```

conda config --add channels conda-forge

```



Once the `conda-forge` channel has been enabled, `elfi` can be installed with:



```

conda install elfi

```



It is possible to list all of the versions of `elfi` available on your platform with:



```

conda search elfi --channel conda-forge

```



### Optional dependencies



- `graphviz` for drawing graphical models (needs [Graphviz](http://www.graphviz.org)), highly recommended



### Installing Python 3



If you are new to Python, perhaps the simplest way to install a specific version of Python

is with [Anaconda](https://www.continuum.io/downloads).



### Virtual environment using Anaconda



It is very practical to create a virtual Python environment. This way you won't interfere

with your default Python environment and can easily use different versions of Python

in different projects. You can create a virtual environment for ELFI using anaconda with:



```

conda create -n elfi python=3.9 numpy

source activate elfi

pip install elfi

```



### Docker container



A 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.



```

git clone --depth 1 https://github.com/elfi-dev/elfi.git

cd elfi

make docker-build  # builds the image with requirements for dev

make docker  # runs a container with live elfi directory

```



To open a Jupyter notebook, run

```

jupyter notebook --ip 0.0.0.0 --no-browser --allow-root

```

within the container and then on host open the page http://localhost:8888. 



### Potential problems with installation



ELFI depends on several other Python packages, which have their own dependencies.

Resolving these may sometimes go wrong:

- If you receive an error about missing `numpy`, please install it first.

- If you receive an error about `yaml.load`, install `pyyaml`.

- On OS X with Anaconda virtual environment say `conda install python.app` and then use

`pythonw` instead of `python`.

- Note that ELFI requires Python 3.9 or greater so try `pip3 install elfi`.

- Make sure your Python installation meets the versions listed in `requirements.txt`.



Citation

--------



If you wish to cite ELFI, please use the paper in [JMLR](http://www.jmlr.org/papers/v19/17-374.html):



```

@article{JMLR:v19:17-374,

  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},

  title   = {ELFI: Engine for Likelihood-Free Inference},

  journal = {Journal of Machine Learning Research},

  year    = {2018},

  volume  = {19},

  number  = {16},

  pages   = {1-7},

  url     = {http://jmlr.org/papers/v19/17-374.html}

}

```