Uncertainty quantification for Bayesian Neural Networks

本项目的目的是设计有效的马尔可夫链蒙特卡罗方法来估计贝叶斯神经网络(NN)的未知参数。神经网络具有数以千计的参数，目前的MCMC方法难以解决这种大小的问题。除了新的MCMC方法外，我们还将验证在实际神经网络学习问题上的性能，例如在强化学习和工程和应用数学的其他适当应用中的性能，并在可能的情况下从理论上验证性能。鉴于目前神经网络在应用科学中的广泛使用，这项工作在许多领域都有潜在的影响。

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.