Gibbs Sampler

The Gibbs sampler is a Markov Chain Monte Carlo (MCMC) method used to draw samples from probability distributions, particularly those that are difficult to sample from directly, aiming to approximate the target distribution. Current research focuses on improving the efficiency and scalability of Gibbs sampling, particularly for high-dimensional data and complex models like Bayesian neural networks and Dirichlet Process Mixture Models, often employing techniques like collapsed Gibbs sampling and distributed computing. These advancements are impacting diverse fields, enabling more accurate Bayesian inference in applications ranging from federated learning and image processing to financial modeling and causal inference.