A carefully regularized neural network trained on the BAR residual recovers Laplace transforms of high-dimensional RBM stationary distributions well enough for near-exact tail-probability inversion up to 30 dimensions.
A unified framework for numerically inverting laplace trans- forms.INFORMS Journal on Computing, 18(4):408–421, 2006
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Deep Learning Method for Stationary Distribution of Reflected Brownian Motion
A carefully regularized neural network trained on the BAR residual recovers Laplace transforms of high-dimensional RBM stationary distributions well enough for near-exact tail-probability inversion up to 30 dimensions.