# An Extendable Python Implementation of Robust Optimisation Monte Carlo

arXiv: https://arxiv.org/abs/2309.10612 · HTML: https://mlsanity.com/paper/2309.10612/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows](https://mlsanity.com/paper/1805.07226/) (2018)
- [Likelihood-free inference by ratio estimation](https://mlsanity.com/paper/1611.10242/) (2016)
- [Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models](https://mlsanity.com/paper/1501.03291/) (2015)
- [ELFI: Engine for Likelihood-Free Inference](https://mlsanity.com/paper/1708.00707/) (2017)
- [Adaptive Gaussian Copula ABC](https://mlsanity.com/paper/1902.10704/) (2019)
- [Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference](https://mlsanity.com/paper/1506.03693/) (2015)
- [Robust Optimisation Monte Carlo](https://mlsanity.com/paper/1904.00670/) (2019)
- [A Likelihood-Free Reverse Sampler of the Posterior Distribution](https://mlsanity.com/paper/1506.04017/) (2015)

## Built on by (most cited first, 1 of 1)

- [Fast and Robust Simulation-Based Inference With Optimization Monte Carlo](https://mlsanity.com/paper/2511.13394/) (2025)

## Abstract (arXiv)

Performing inference in statistical models with an intractable likelihood is challenging, therefore, most likelihood-free inference (LFI) methods encounter accuracy and efficiency limitations. In this paper, we present the implementation of the LFI method Robust Optimisation Monte Carlo (ROMC) in the Python package ELFI. ROMC is a novel and efficient (highly-parallelizable) LFI framework that provides accurate weighted samples from the posterior. Our implementation can be used in two ways. First, a scientist may use it as an out-of-the-box LFI algorithm; we provide an easy-to-use API harmonized with the principles of ELFI, enabling effortless comparisons with the rest of the methods included in the package. Additionally, we have carefully split ROMC into isolated components for supporting extensibility. A researcher may experiment with novel method(s) for solving part(s) of ROMC without reimplementing everything from scratch. In both scenarios, the ROMC parts can run in a fully-parallelized manner, exploiting all CPU cores. We also provide helpful functionalities for (i) inspecting the inference process and (ii) evaluating the obtained samples. Finally, we test the robustness of our implementation on some typical LFI examples.