Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings

Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah, Florence Forbes, Umberto Picchini

Abstract: Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators. These approaches are collectively known as "simulation-based inference" (SBI). Recent SBI methods have made use of neural networks (NN) to provide approximate, yet expressive constructs for the unavailable likelihood function and the posterior distribution. However, the trade-off between accuracy and computational demand leaves much space for improvement. In this work, we propose an alternative that provides both approximations to the likelihood and the posterior distribution, using structured mixtures of probability distributions. Our approach produces accurate posterior inference when compared to state-of-the-art NN-based SBI methods, even for multimodal posteriors, while exhibiting a much smaller computational footprint. We illustrate our results on several benchmark models from the SBI literature and on a biological model of the translation kinetics after mRNA transfection.

Submission Length: Long submission (more than 12 pages of main content)

Changes Since Last Submission: In the last version, we accidentally commented out the sentence appearing on top of page 9, reading "the $N$ retained draws via MH are $N$ post-burnin draws, where the burnin consists of 100 iterations", which instead appeared in all previous versions.

Code: https://github.com/henhagg/semple

Assigned Action Editor: ~Alp_Kucukelbir1

Submission Number: 2404