Non-parametric Bayesian Learning with Deep Learning Structure and Its Applications in Wireless Networks

Statistical models have been applied to the classification and prediction problems in machine learning and data analysis [1]. Some statistical methods make the hypothesis of mathematical models that are controlled by certain parameters to fit the latent structure of observed data [2]. The observed data are assumed to be generated by complex structures which have hierarchical layers and hidden causes [3]. One key challenge faced by modeling the data structure in this way is thus the determination of the numbers of layers and hidden variables. However, it is sometimes impractical and challenging to choose any fixed number for the model structure when making the hypothesis.