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

• URL: http://arxiv.org/abs/2403.07454v3
• Date: Sat, 22 Jun 2024 09:26:27 GMT
• Title: Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings
• Authors: Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah, Florence Forbes, Umberto Picchini,
• Abstract summary: We propose an alternative to "simulation-based inference" ( SBI) that provides both approximations to the likelihood and the posterior distribution. Our approach produces accurate posterior inference when compared to state-of-the-art NN-based SBI methods, even for multimodal posteriors. We illustrate our results on several benchmark models from the SBI literature and on a biological model of the translation kinetics after mRNA transfection.
• Score: 0.820217860574125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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