Maximum a Posteriori

Maximum a posteriori (MAP) estimation is a statistical method aiming to find the most probable explanation for observed data given a prior model. Current research focuses on applying MAP to diverse problems, including image denoising and restoration using graph-based deep denoisers and plug-and-play architectures, personalized federated learning via bi-level optimization, and efficient state estimation in robotics through sequential variational inference methods. These advancements improve model accuracy, robustness, and computational efficiency across various applications, impacting fields like computer vision, machine learning, and robotics.