Ensemble Kalman Inversion for General Likelihoods
Not on the map. Why?
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.
4 papers.
- 2023Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversioncited by 6
- 2022Bayesian Learning of Parameterised Quantum Circuitscited by 3
- 2024Thermodynamic Bayesian Inferencecited by 2
- 2025Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networkscited by 2
- 2025Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks15 Jan 2025
- 2024Thermodynamic Bayesian Inference2 Oct 2024
- 2023Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion13 Mar 2023
- 2022Bayesian Learning of Parameterised Quantum Circuits15 Jun 2022