Joint Estimation

Joint estimation focuses on simultaneously inferring multiple related parameters or variables from observed data, improving accuracy and efficiency compared to separate estimations. Current research emphasizes developing sophisticated algorithms, including those based on graph neural networks, Bayesian networks, and transformers, to handle complex dependencies between variables across diverse data types (e.g., spatiotemporal, multi-modal). These advancements have significant implications for various fields, enabling improved predictions in areas such as urban logistics, music transcription, and human motion capture, as well as facilitating more accurate modeling of complex systems like batteries and brain networks.