Generative diffusions are a powerful class of Monte Carlo samplers that leverage bridging Markov processes to approximate complex, high-dimensional distributions, such as those found in image processing and language models. Despite their success in these domains, an important open challenge remains: extending these techniques to sample from conditional distributions, as required in, for example, Bayesian inverse problems. In this paper, we present a comprehensive review of existing computational approaches to conditional sampling within generative diffusion models. Specifically, we highlight key methodologies that either utilise the joint distribution, or rely on (pre-trained) marginal distributions with explicit likelihoods, to construct conditional generative samplers.
@article{arxiv.2409.09650,
title = {Conditional sampling within generative diffusion models},
author = {Zheng Zhao and Ziwei Luo and Jens Sjölund and Thomas B. Schön},
journal= {arXiv preprint arXiv:2409.09650},
year = {2025}
}