A new variational inference method uses neural networks to tilt Lévy measures, enabling scalable posterior inference for jump processes while preserving their discontinuous structure.
A multilevel Monte Carlo algorithm for Lévy-driven stochastic differential equations.Stochastic Processes and their Applications, 121(7):1565– 1587
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Variational Inference for L\'evy Process-Driven SDEs via Neural Tilting
A new variational inference method uses neural networks to tilt Lévy measures, enabling scalable posterior inference for jump processes while preserving their discontinuous structure.