Gibbs Distribution

Gibbs distributions, probability distributions proportional to the exponential of a negative energy function, are central to statistical mechanics, machine learning, and Bayesian inference. Current research focuses on developing efficient algorithms for sampling from and estimating parameters of Gibbs distributions, particularly in high-dimensional and non-convex settings, employing methods like Particle Gibbs samplers, Langevin Monte Carlo, and belief propagation. These advancements are crucial for tackling complex problems in diverse fields, including image denoising, cosmology, and the optimization of neural networks, by enabling accurate inference and efficient computation in challenging scenarios.