Multivariate Gaussian

Multivariate Gaussian distributions are a fundamental tool in statistics and machine learning, used to model the joint probability of multiple correlated variables. Current research focuses on efficiently handling high-dimensional data and complex scenarios, including developing novel algorithms like Approximate Message Passing and employing structured Gaussian models (e.g., sparse Cholesky decompositions) within larger architectures such as Gaussian splatting for improved computational efficiency and uncertainty quantification. These advancements are crucial for addressing challenges in diverse fields, from astrophysics and 3D scene modeling to robust regression and anomaly detection, where accurate modeling of uncertainty and complex dependencies is paramount.