Error Bounds for a Diffusion Model-Based Drift Estimator
Parameter estimation in stochastic differential equations is a classical statistical problem of much importance in many scientific fields. Recent work of Tapia Costa et al. (2026) introduced a novel technique for estimating the drift when the diffusion parameter is known, using discrete samples from multiple trajectories. Their method treats drift estimation as a denoising problem, and leverages tools from (conditional) score-matching diffusion models. Although their experiments showed promising results across different drift classes, the question of theoretical guarantees for their estimator was left unanswered. In this note, we address this gap by exploiting techniques from diffusion mo...
| Authors | Ioar Casado-Telletxea, Omar Rivasplata |
|---|---|
| Primary category | stat.ML |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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