For incomplete data problems, directly predicting conditional marginals with a Transformer can be faster and more accurate than traditional statistical methods or full generative models, enabling better decision-making under uncertainty.
Marformer is a Transformer model that predicts conditional probability distributions over missing variables given observed data. Unlike generative models, it directly outputs marginal distributions needed for decision-making under uncertainty, without modeling the full joint distribution. It's trained like BERT to predict missing values and works in a single forward pass.