Leveraging generative models to assist Monte Carlo sampling
Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation. Despite decades of methodological developments, two major challenges remain: scaling to high dimensions and efficiently exploring multimodal distributions characterized by metastable states. Classical approaches such as Markov chain Monte Carlo, tempering methods, or enhanced sampling based on collective variables have achieved major successes, but they also face intrinsic limitations. This tutorial review explores a new paradigm that has recently emerged at the interface of machine learning and co...
| Authors | Marylou Gabrié |
|---|---|
| Primary category | stat.ML |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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