Gaussian Graphical Model

Gaussian graphical models (GGMs) represent conditional dependencies between variables as a graph, with edges signifying statistical relationships. Current research focuses on improving the scalability of GGM estimation for high-dimensional datasets, often employing algorithms like cyclic block coordinate descent or leveraging compressive sensing techniques to reduce computational complexity. Furthermore, research addresses challenges in model selection, including the limitations of cross-validation and the development of robust methods for handling noisy data and identifying underlying graph structures, even in the presence of inherent ambiguities. These advancements are crucial for analyzing complex datasets across diverse fields, enabling more accurate and efficient inference of relationships within large-scale networks.