A Splitting Method for SDE Terminal-Law Estimation
In many settings involving stochastic differential equations, including in diffusion based generative AI, our aim is to accurately generate samples from a terminal distribution. Typically, this is done by generating i.i.d. samples of diffusion paths. Given a fixed simulation budget, a reasonable way to gain efficiency may be to instead generate a tree of paths through appropriately split partial paths. This suggests improved performance, but one worries about the injected dependence. In this paper, we study this issue comprehensively. With Kolmogorov-Smirnov distance as a measure of accuracy, we identify the limiting errors of the associated empirical distributions as the simulation budge...
| Authors | Rushil Gupta, Sandeep Juneja |
|---|---|
| Primary category | stat.ML |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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