Markov Chain Monte Carlo

Markov Chain Monte Carlo (MCMC) methods are computational techniques used to sample from complex probability distributions, primarily for Bayesian inference and other statistical tasks. Current research focuses on improving MCMC efficiency and scalability, particularly through the integration of neural networks (e.g., GFlowNet, diffusion models) and adaptive algorithms (e.g., annealed MCMC, policy gradient methods) to address challenges in high-dimensional spaces and multimodal distributions. These advancements are significantly impacting diverse fields, enabling more accurate and efficient inference in applications ranging from drug discovery and inverse problem solving to epidemiological modeling and machine learning.