Structured Neural SDEs for Functional Calibration
SLiSDE, a family of Neural SDE models built from structured linear stochastic layers, is introduced and it is proved well-posedness, a discretisation error bound, validity of the change of measure, and a universality result: the terminal laws of the gated stack are dense in the space of square-integrable laws.
Francesco Piatti, Andrea Iannucci, Thomas Cass
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