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Preprints, Working Papers, ... Year : 2024

Diffusion posterior sampling for simulation-based inference in tall data settings

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.
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Dates and versions

hal-04459545 , version 1 (15-02-2024)
hal-04459545 , version 2 (11-04-2024)
hal-04459545 , version 3 (07-06-2024)

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Identifiers

  • HAL Id : hal-04459545 , version 3

Cite

Julia Linhart, Gabriel Victorino Cardoso, Alexandre Gramfort, Sylvain Le Corff, Pedro Luiz Coelho Rodrigues. Diffusion posterior sampling for simulation-based inference in tall data settings. 2024. ⟨hal-04459545v3⟩
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