Gaussian Model

Gaussian models are probabilistic models that assume data is normally distributed, enabling efficient statistical inference and prediction. Current research focuses on extending Gaussian models to handle complex data structures, such as incorporating censored data, learning Bayesian networks, and representing 3D shapes and images via Gaussian splatting or mixtures of Gaussian processes. These advancements are improving uncertainty quantification in machine learning, enabling more efficient algorithms for various tasks (e.g., drug discovery, image-to-3D generation, and object detection), and providing a foundation for understanding the behavior of neural networks.