Generalised Bayesian

Generalized Bayesian inference offers a robust framework for Bayesian updating, addressing limitations of standard Bayesian methods when dealing with outliers, model misspecification, or high dimensionality. Current research focuses on developing computationally efficient algorithms, such as those incorporating Kalman filtering or Gaussian processes, that maintain the robustness of the generalized Bayesian approach while achieving scalability for large datasets and complex models. This approach is proving valuable across diverse applications, including online changepoint detection, object tracking, and Bayesian optimization, by providing more reliable and accurate inferences in challenging scenarios. The resulting improvements in robustness and efficiency are significant for various scientific fields and practical applications.