Tag
This paper presents a deep learning approach that learns the Laplace transform of high-dimensional reflected Brownian motion (RBM) stationary distributions using the basic adjoint relationship. The method demonstrates near-perfect prediction in high-dimensional settings where analytical solutions are unavailable.
An educational thread explaining the mathematical foundations used by quantitative trading firms like Renaissance Technologies, covering concepts from Bernoulli to Brownian motion.