Inference for stochastic differential equations driven by weighted sub-fractional Brownian motion using neural networks and the Euler approximation

arXiv:2610.00793v1 Announce Type: new Abstract: We consider the estimation of drift, diffusion, and noise covariance from discrete observations of stochastic differential equations driven by Gaussian processes. For a fixed observation horizon $T>0$ and a known initial state $x_0\in\mathbb R$, we st…

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1. 2026-10-02 04:00 · arXiv stat.ML
   Inference for stochastic differential equations driven by weighted sub-fractional Brownian motion using neural networks and the Euler approximation