# ELFI: Engine for Likelihood-Free Inference

arXiv: https://arxiv.org/abs/1708.00707 · HTML: https://mlsanity.com/paper/1708.00707/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 10 of 10)

- [Benchmarking Simulation-Based Inference](https://mlsanity.com/paper/2101.04653/) (2021)
- [BayesFlow: Amortized Bayesian Workflows With Neural Networks](https://mlsanity.com/paper/2306.16015/) (2023)
- [Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference](https://mlsanity.com/paper/2301.13368/) (2023)
- [Preconditioned Neural Posterior Estimation for Likelihood-free Inference](https://mlsanity.com/paper/2404.13557/) (2024)
- [Intuitive and Efficient Human-robot Collaboration via Real-time Approximate Bayesian Inference](https://mlsanity.com/paper/2205.08657/) (2022)
- [Stochastic Cluster Embedding](https://mlsanity.com/paper/2108.08003/) (2021)
- [Likelihood-Free Inference in State-Space Models with Unknown Dynamics](https://mlsanity.com/paper/2111.01555/) (2021)
- [An Extendable Python Implementation of Robust Optimisation Monte Carlo](https://mlsanity.com/paper/2309.10612/) (2023)
- [Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output](https://mlsanity.com/paper/2205.10890/) (2022)
- [Likelihood-free Model Choice for Simulator-based Models with the Jensen--Shannon Divergence](https://mlsanity.com/paper/2206.04110/) (2022)

## Abstract (arXiv)

Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components in LFI, such as priors, simulators, summaries or distances, to a network called ELFI graph. The components can be implemented in a wide variety of languages. The stand-alone ELFI graph can be used with any of the available inference methods without modifications. A central method implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference (BOLFI), which has recently been shown to accelerate likelihood-free inference up to several orders of magnitude by surrogate-modelling the distance. ELFI also has an inbuilt support for output data storing for reuse and analysis, and supports parallelization of computation from multiple cores up to a cluster environment. ELFI is designed to be extensible and provides interfaces for widening its functionality. This makes the adding of new inference methods to ELFI straightforward and automatically compatible with the inbuilt features.