McMc Sampling

Markov Chain Monte Carlo (MCMC) sampling is a cornerstone of Bayesian inference, aiming to efficiently draw samples from complex probability distributions for statistical analysis and model training. Current research focuses on improving MCMC's efficiency and scalability, particularly for high-dimensional problems, through techniques like adaptive Metropolis-Hastings algorithms and the integration of normalizing flows. These advancements are crucial for tackling challenges in diverse fields, including generative modeling (e.g., image generation), causal inference (e.g., learning directed acyclic graphs), and Bayesian neural networks, enabling more accurate and computationally feasible analyses of large datasets.