BibTeX

@article{2404.07593v2,
Author        = {Julia Linhart and Gabriel Victorino Cardoso and Alexandre Gramfort and Sylvain Le Corff and Pedro L. C. Rodrigues},
Title         = {Diffusion posterior sampling for simulation-based inference in tall data settings},
Eprint        = {2404.07593v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {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.},
Year          = {2024},
Month         = {Apr},
Url           = {http://arxiv.org/abs/2404.07593v2},
File          = {2404.07593v2.pdf}
}