Gaussian Density

Gaussian density estimation is a core problem in statistics and machine learning, focusing on accurately modeling and representing data distributions using Gaussian functions or their generalizations. Current research emphasizes improving the efficiency and robustness of estimation methods, particularly for dynamic data, by exploring optimal weighting schemes in sliding window approaches and employing generalized Gaussian densities to handle diverse data characteristics. These advancements are impacting diverse fields, including computer vision (e.g., object counting), signal processing, and generative modeling, where they enable more accurate and efficient algorithms for tasks like image generation and system identification.