Langevin Sampler

Langevin samplers are Markov Chain Monte Carlo (MCMC) methods used to efficiently sample from complex probability distributions, particularly in high-dimensional spaces, by simulating a stochastic differential equation. Current research focuses on improving the robustness and efficiency of these samplers, addressing issues like hyperparameter sensitivity and convergence rates through techniques such as preconditioning (e.g., using Fisher information), bias correction, and the development of novel algorithms like stochastic gradient Barker dynamics. These advancements are significant for various applications, including Bayesian inference, generative modeling, and optimization problems across diverse fields, offering improved accuracy and scalability for complex sampling tasks.