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Fast ABC with joint generative modelling and subset simulation
Editeur(s)
David Ginsbourger
Niklas Linde
Date de parution
2022
In
Lecture Notes in Computer Science
Vol.
13163
De la page
413
A la page
429
Revu par les pairs
true
Résumé
We propose a novel approach for solving inverse-problems with high-dimensional inputs and an expensive forward mapping. It leverages joint deep generative modelling to transfer the original problem spaces to a lower dimensional latent space. By jointly modelling input and output variables and endowing the latent with a prior distribution, the fitted probabilistic model indirectly gives access to the approximate conditional distributions of interest. Since model error and observational noise with unknown distributions are common in practice, we resort to likelihood-free inference with Approximate Bayesian Computation (ABC). Our method calls on ABC by Subset Simulation to explore the regions of the latent space with dissimilarities between generated and observed outputs below prescribed thresholds. We diagnose the diversity of approximate posterior solutions by monitoring the probability content of these regions as a function of the threshold. We further analyze the curvature of the resulting diagnostic curve to propose an adequate ABC threshold. When applied to a cross-borehole geophysical example, our approach delivers promising performance without using prior knowledge of the forward nor of the noise distribution.
Nom de l'événement
Machine Learning, optimization and data science, LOD2021
Lieu
Grasmere, Lake District, England – UK
Identifiants
Autre version
https://lod2021.icas.cc/
Type de publication
conference paper
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