Bayesian Approach

Bayesian approaches offer a powerful framework for incorporating uncertainty into various machine learning tasks, aiming to improve model robustness, interpretability, and reliability. Current research focuses on applying Bayesian methods to diverse areas, including clustering, regression (especially for circular data and PDEs), and reinforcement learning, often employing techniques like Thompson sampling, Markov Chain Monte Carlo, and variational inference within models such as Bayesian neural networks and Gaussian processes. This focus on uncertainty quantification and robust inference has significant implications for numerous fields, enhancing the trustworthiness of predictions in applications ranging from audio recognition and traffic prediction to structural health monitoring and public health interventions.