Joint Distribution

Joint distribution estimation focuses on accurately determining the probability of multiple variables occurring together, a crucial task across numerous scientific fields. Current research emphasizes developing efficient algorithms and models, including generative flows, tensor decompositions, and optimal transport methods, to estimate these distributions even with high-dimensional data or limited paired samples. These advancements are improving the accuracy and scalability of probabilistic modeling, impacting applications ranging from causal inference and few-shot learning to robotic path planning and time series forecasting. The ability to effectively model joint distributions is increasingly vital for extracting insights from complex datasets and building more robust and accurate predictive models.