Stein's method has emerged as a critical framework in the study of distributional approximations, providing quantitative bounds between probability distributions through the formulation and solution ...

Deep learning has emerged as a transformative paradigm in modern computational science, leveraging neural networks to approximate complex functions across a variety of domains. Central to this ...

This paper offers a Bayesian framework for the calibration of financial models using neural stochastic differential equations ...