Diffusion posterior sampling for simulation-based inference in tall data settings
<|reference_start|>Diffusion posterior sampling for simulation-based inference in tall data settings: Determining which parameters of a non-linear model best describe a set of experimental data is a fundamental problem in science and it has gained much traction lately with the rise of complex large-scale simulators. The likelihood of such models is typically intractable, which is why classical MCMC methods can not be used. Simulation-based inference (SBI) stands out in this context by only requiring a dataset of simulations to train deep generative models capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. The proposed method is built upon recent developments from the flourishing score-based diffusion literature and allows to estimate the tall data posterior distribution, while simply using information from a score network trained for a single context observation. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.<|reference_end|>
@article{linhart2024diffusion,
    title={Diffusion posterior sampling for simulation-based inference in tall data
  settings},
    author={Julia Linhart, Gabriel Victorino Cardoso, Alexandre Gramfort, Sylvain
  Le Corff, Pedro L. C. Rodrigues},
    journal={arXiv preprint arXiv:2404.07593},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07593},
    primaryClass={stat.ML cs.LG stat.ME}
}