Sequential Inference

Sequential inference focuses on updating probabilistic models and making predictions as new data arrives incrementally, aiming for efficient and accurate estimations in dynamic environments. Current research emphasizes developing computationally efficient algorithms, such as those leveraging structured mixtures of probability distributions or modified Vision Transformers, and improving the robustness of methods like simulation-based inference and test-time training, often through techniques like anchored clustering. These advancements are crucial for diverse applications, including real-time decision support in healthcare, adaptive experimental design, and robust model deployment in scenarios with distribution shifts.