Large Scale Bayesian

Large-scale Bayesian inference tackles the challenge of efficiently approximating complex posterior distributions in high-dimensional datasets, often employing variational inference (VI) or Markov Chain Monte Carlo (MCMC) methods as computational alternatives. Current research focuses on improving the convergence rates and theoretical guarantees of VI algorithms, including coordinate ascent and black-box approaches, and developing differentially private MCMC methods for sensitive data. These advancements are crucial for enabling Bayesian methods in applications with massive datasets, improving both the accuracy and efficiency of statistical inference across diverse scientific fields.