Bayesian Posterior

Bayesian posterior estimation aims to quantify uncertainty in model parameters by calculating the probability distribution of those parameters given observed data. Current research focuses on developing efficient algorithms, such as Markov Chain Monte Carlo (MCMC) methods and variational inference, often coupled with neural networks or other advanced architectures like graph neural networks, to handle high-dimensional and complex models, particularly in deep learning and Bayesian inverse problems. These advancements improve uncertainty quantification in diverse fields, ranging from action recognition and material science to scientific modeling and decision-making under uncertainty. The resulting improved accuracy and efficiency of Bayesian inference have significant implications for various scientific disciplines and practical applications.