# Ensemble Kalman Inversion for General Likelihoods

arXiv: https://arxiv.org/abs/2110.03034 · HTML: https://mlsanity.com/paper/2110.03034/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2021.

## Built on by (most cited first, 5 of 5)

- [Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion](https://mlsanity.com/paper/2303.07392/) (2023)
- [Bayesian Learning of Parameterised Quantum Circuits](https://mlsanity.com/paper/2206.07559/) (2022)
- [Thermodynamic Bayesian Inference](https://mlsanity.com/paper/2410.01793/) (2024)
- [Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2501.08501/) (2025)
- [Robust Ensemble Guidance for Scientific Inverse Problems](https://mlsanity.com/paper/2610.05371/) (2026)

## Abstract (arXiv)

In this letter we generalise Ensemble Kalman inversion techniques to general Bayesian models where previously they were restricted to additive Gaussian likelihoods - all in the difficult setting where the likelihood can be sampled from, but its density not necessarily evaluated.