Sample Moment

Sample moments, statistical summaries of data distributions, are central to various fields, with current research focusing on efficiently extracting representative scenarios from high-dimensional data and improving the accuracy of estimations using these moments. Researchers are exploring novel algorithms, including those based on Bayesian inference and physics-informed neural networks, to enhance the precision and efficiency of moment-based estimations, particularly for complex data like graphs and stochastic systems. These advancements have implications for diverse applications, such as portfolio optimization, sensor tasking, and density estimation, by enabling more robust and accurate modeling of uncertainty and complex systems.