Online Estimation

Online estimation focuses on developing algorithms that continuously update parameter estimates as new data arrive, in contrast to traditional batch methods. Current research emphasizes robust methods handling noisy, incomplete, or outlier-ridden data, often employing deep learning, Kalman filtering variants, or fuzzy logic systems depending on the application. These advancements are crucial for real-time applications across diverse fields, including robotics, finance, and signal processing, enabling more adaptive and efficient systems. The development of computationally efficient and statistically sound online estimators remains a key focus.