Uncertainty quantification for Bayesian Neural Networks

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The aim of this project is devise efficient Markov Chain Monte Carlo methods to estimate the unknown parameters of a Bayesian Neural Network (NN.) NNs have parameters that number many thousands and present MCMC methods struggle with problems of this size. In addition to new MCMC methodology, we will verify performance on practical NN learning problems, e.g. in Reinforcement Learning and other suitable applications from engineering and applied mathematics, and also verify the performance theoretically where we can. This work is potentially impactful in many areas given the current wide spread use of NN in the applied sciences.