Ensemble Kalman Inversion for General Likelihoods

Abstract

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.

References in corpus (2)

• The Ensemble Kalman Filter for Inverse Problems
• Robust Generalised Bayesian Inference for Intractable Likelihoods

Cited by in corpus (3)

• Bayesian Learning of Parameterised Quantum Circuits
• Guided sequential ABC schemes for intractable Bayesian models
• Sequential Kalman Tuning of the $t$-preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems