Transport Metric

Transport metrics, particularly variants of the Wasserstein metric, are increasingly used to compare and analyze probability distributions, offering a powerful framework for diverse applications. Current research focuses on developing efficient algorithms for computing these metrics, especially in high-dimensional spaces and under constraints like missing data or distributional shape restrictions, often leveraging optimal transport theory and maximum entropy principles. This approach finds utility in various fields, including robust Bayesian inference, fair machine learning, and analyzing complex datasets with limited samples, enabling improved statistical modeling and more reliable inferences.