**摘要**
Modelling extreme events and heavy-tailed phenomena is central to building reliable predictive systems in domains such as finance, climate science, and safety-critical AI. While Lévy processes provide a natural mathematical framework for capturing jumps and heavy tails, Bayesian inference for Lévy-driven stochastic differential equations (SDEs) remains intractable with existing methods: Monte Carl
👤 作者: Yaman Kindap, Manfred Opper, Benjamin Dupuis, Umut Simsekli, Tolga Birdal

---
🔗 **[通过神经倾斜对Lévy过程驱动的SDE进行变分推断](https://arxiv.org/abs/2605.10934v1)**

> Variational Inference for Lévy Process-Driven SDEs via Neural Tilting
🏷️ 来源: ArXiv cs.AI
⏱️ 2026-05-13 08:00