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. ELFI features an easy to use generative modeling syntax and supports parallelized inference out of the box.
See the quickstart to get started.
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:
- Bayesian Optimization
- No-U-Turn-Sampler, a Hamiltonian Monte Carlo MCMC sampler
Other names or related approaches to LFI include simulator-based inference, approximate Bayesian inference, indirect inference, etc.