High Dimensional Graphical Model

High-dimensional graphical models aim to represent complex relationships between numerous variables by constructing graphs where nodes represent variables and edges signify dependencies. Current research focuses on developing efficient algorithms for learning these models, particularly for diverse data types (continuous, discrete, mixed) and time series data, often employing techniques like latent Gaussian copula models, sparse-group lasso, and deep learning-based approaches for score function approximation. These advancements improve the accuracy and scalability of inference, enabling applications in diverse fields such as genomics, image analysis, and risk assessment, where understanding intricate variable interactions is crucial.