Density Estimation

Density estimation, the task of inferring the probability distribution of a dataset, is crucial for numerous applications across science and engineering. Current research emphasizes developing robust and efficient methods, particularly for high-dimensional data and complex distributions, focusing on techniques like kernel density estimation, normalizing flows, and neural network-based approaches including transformers and diffusion models. These advancements are improving the accuracy and efficiency of density estimation in diverse fields, ranging from synthetic data generation and anomaly detection to Bayesian inference and time series analysis. Furthermore, research is actively addressing challenges such as privacy preservation and handling concept drift in dynamic data.