Papers › Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks

Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks

12 May 2021arXiv:2105.05650archive 2025-07-28

Dian Wu, Riccardo Rossi, Giuseppe Carleo

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to such distributions, but suffer from either intrinsic bias or high variance. In this Letter, we propose a way to make this approximation unbiased and with low variance. Our method uses physical symmetries and variable-size cluster updates which utilize the structure of autoregressive factorization. We test our method for first- and second-order phase transitions of classical spin systems, showing its viability for critical systems and in the presence of metastable states.

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