Bayesian Update

Bayesian update methods refine probabilistic beliefs by incorporating new data, aiming to improve accuracy and efficiency in diverse applications. Current research focuses on enhancing robustness to outliers and model misspecifications, particularly within Kalman filtering and other online learning algorithms, as well as developing efficient Bayesian updates for high-dimensional data and complex models like deep neural networks. These advancements are impacting fields ranging from structural engineering and tsunami prediction to dynamic pricing and reinforcement learning, enabling more adaptive and accurate decision-making in uncertain environments.