Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC

Alina Bazarova, Johann Fredrik Jadebeck, Henrik Zunker, Carolina J. Klett-Tammen, Torben Heinsohn et al.|June 25, 2026arXiv

Key Takeaway

Neural simulation-based inference can replace slow MCMC for fitting complex disease models, running 15-120x faster on GPUs while producing nearly identical results—enabling real-time outbreak analysis.

Summary

This paper compares simulation-based inference (SBI) with traditional MCMC methods for fitting epidemiological models to COVID-19 data. SBI uses neural networks to learn the relationship between model parameters and data, enabling much faster Bayesian inference—achieving 15-120x speedups while maintaining accuracy comparable to MCMC.

training efficiency evaluation

Key Terms

bayesian-inference markov-chain-monte-carlo simulation-based-inference neural-posterior-estimation wasserstein-distance