General-purpose use of Bayesian learning for hierarchical probabilistic models

这个项目的目的是建立贝叶斯学习的通用用途，用于分层概率模型，如神经网络和隐马尔可夫模型。我们使用交换蒙特卡罗方法来有效地执行贝叶斯学习，并通过并行化交换蒙特卡罗方法显著地提高了计算成本。此外，我们构造了交换蒙特卡罗方法的最优设计，并将该方法应用于径向基函数网络的谱反卷积。

The purpose of this project is to establish a general-purpose use of Bayesian learning for hierarchical probabilistic model such as a neural network and a hidden Markov model. We use an exchange Monte Carlo method for performing Bayesian learning efficiently, and significantly improve the computational cost by parallelizing the exhcnage Monte Carlo method. Moreover, we construct a optimal design of the exchange Monte Carlo method and apply the proposed method to the spectral deconvolution for the radial basis function networks.