Non Gaussian

Non-Gaussian research focuses on developing methods to model and analyze data that deviate from the standard bell-curve (Gaussian) distribution, a common assumption often violated in real-world datasets. Current research emphasizes robust algorithms and model architectures, including deep learning approaches, diffusion models, and kernel methods, to effectively capture complex, non-Gaussian uncertainty and higher-order statistical dependencies. These advancements are crucial for improving the accuracy and reliability of various applications, such as robotic estimation, speech enhancement, and parameter estimation in complex systems, where non-Gaussianity is prevalent. The development of efficient and scalable methods for handling non-Gaussian data is driving significant progress across numerous scientific disciplines.