Papers › Nonlinear MCMC for Bayesian Machine Learning

Nonlinear MCMC for Bayesian Machine Learning

11 Feb 2022arXiv:2202.05621archive 2025-07-28

James Vuckovic

We explore the application of a nonlinear MCMC technique first introduced in [1] to problems in Bayesian machine learning. We provide a convergence guarantee in total variation that uses novel results for long-time convergence and large-particle ("propagation of chaos") convergence. We apply this nonlinear MCMC technique to sampling problems including a Bayesian neural network on CIFAR10.

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