ELFI - Engine for Likelihood-Free Inference

ELFI is a statistical software package for likelihood-free inference (LFI) such as Approximate Bayesian Computation (ABC). The term LFI refers to a family of inference methods that replace the use of the likelihood function with a data generating simulator function. Other names or related approaches to LFI include simulator-based inference, approximate Bayesian inference, indirect inference, etc.

ELFI features an easy to use syntax and supports parallelized inference out of the box.

MA2 model in ELFI

See the quickstart to get started.

ELFI is licensed under BSD3. The source is in GitHub.

Currently implemented LFI methods:

  • ABC rejection sampler
  • Sequential Monte Carlo ABC sampler
  • Bayesian Optimization for Likelihood-Free Inference (BOLFI) framework

ELFI also has the following non LFI methods:

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

Developer documentation


If you wish to cite ELFI, please use the paper in arXiv:

Author = {Jarno Lintusaari and Henri Vuollekoski and Antti Kangasrääsiö and Kusti Skytén and Marko Järvenpää and Pekka Marttinen and Michael Gutmann and Aki Vehtari and Jukka Corander and Samuel Kaski},
Title = {ELFI: Engine for Likelihood Free Inference},
Year = {2018},
Eprint = {arXiv:1708.00707},