Discrete Distribution

Discrete distributions, probability distributions over finite sets, are central to many machine learning and statistical applications, with research focusing on efficiently estimating, learning, and generating these distributions from data. Current efforts involve developing novel algorithms for handling high-dimensional data, addressing challenges posed by data heterogeneity and limited samples, and improving the accuracy and efficiency of existing methods like spectral clustering and Bayesian approaches. These advancements have significant implications for various fields, including reinforcement learning, anomaly detection, and privacy-preserving data synthesis, by enabling more robust and accurate modeling of discrete data in diverse applications.