Updating Beliefs over Eventual Outcomes as Evidence Arrives

Real-world decisions often require updating beliefs about the same eventual outcome as evidence arrives. Learning these belief paths is challenging because the true outcome probabilities at intermediate stages are not directly observable, even after outcomes are known. We introduce **BELIEF**, a generative framework for learning belief paths from event streams. Rather than directly predicting outcomes, BELIEF learns how incoming evidence is distributed under each candidate outcome, given the full preceding history. Observed evidence then updates the relative support for these candidate outcomes through accumulated log likelihoods and Bayesian inference, yielding entire belief paths without direct supervision of intermediate beliefs. It supports binary, multiclass, and continuous outcomes. We provide quantitative guarantees for belief-path recovery and prove exact recovery under correct prior and evidence models. Experiments show accurate belief-path recovery on synthetic evidence streams and the closest alignment with reference probability paths on real data, while preserving downstream task performance. Each belief update decomposes into per-event evidence contributions, providing a basis for risk monitoring and timely decisions.