Non Gaussianity

Non-Gaussianity, the deviation of data from a normal distribution, is a central challenge in many scientific fields, impacting the accuracy and efficiency of statistical modeling and inference. Current research focuses on developing methods to handle non-Gaussian data, including novel algorithms for independent component analysis (ICA) that relax Gaussianity assumptions, hierarchical probabilistic models for high-dimensional data, and simulation-based inference techniques leveraging neural networks to capture non-linear and non-Gaussian features. These advancements are crucial for improving the accuracy of cosmological parameter estimation, modeling complex physical phenomena like turbulence, and enhancing the reliability of causal inference methods in various applications.