Noisy Observation

Noisy observation, the presence of errors or uncertainties in data collection, is a pervasive challenge across numerous scientific fields and applications. Current research focuses on developing robust methods for handling such noise, including probabilistic approaches like Gaussian Processes and Kalman filtering, as well as techniques that leverage sparsity and incorporate noise models directly into optimization algorithms. These advancements are crucial for improving the accuracy and reliability of inferences drawn from noisy data, impacting diverse areas from machine learning and signal processing to robotics and scientific data analysis. The ultimate goal is to extract meaningful information and build reliable models despite the inherent uncertainties in observations.