Fast ABC with Joint Generative Modelling and Subset Simulation

Maalouf, Eliane; Ginsbourger, David; Linde, Niklas (2022). Fast ABC with Joint Generative Modelling and Subset Simulation. In: Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science: Vol. 13163 (pp. 413-429). Cham: Springer International Publishing 10.1007/978-3-030-95467-3_30

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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.

Item Type:

Book Section (Book Chapter)


08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematical Statistics and Actuarial Science

UniBE Contributor:

Ginsbourger, David


300 Social sciences, sociology & anthropology > 360 Social problems & social services
500 Science > 510 Mathematics




Lecture Notes in Computer Science


Springer International Publishing




David Ginsbourger

Date Deposited:

14 Apr 2022 11:08

Last Modified:

05 Dec 2022 16:18

Publisher DOI:


Additional Information:

7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I




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