Incomplete Data

Incomplete data is a pervasive challenge across numerous scientific domains, hindering accurate model training and reliable inference. Current research focuses on developing robust methods to handle missing data, employing techniques like generative adversarial networks (GANs), variational autoencoders (VAEs), and various imputation strategies within diverse model architectures such as neural networks, diffusion models, and staged trees. These advancements are crucial for improving the reliability of analyses in fields ranging from medical diagnosis and environmental monitoring to social science research and industrial condition monitoring, where incomplete datasets are frequently encountered. The ultimate goal is to develop methods that not only effectively handle missing data but also quantify the uncertainty introduced by its presence.