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

摘要

In this paper, we present an infinite hierarchical non-parametric Bayesianmodel to extract the hidden factors over observed data, where the number ofhidden factors for each layer is unknown and can be potentially infinite.Moreover, the number of layers can also be infinite. We construct the modelstructure that allows continuous values for the hidden factors and weights,which makes the model suitable for various applications. We use theMetropolis-Hastings method to infer the model structure. Then the performanceof the algorithm is evaluated by the experiments. Simulation results show thatthe model fits the underlying structure of simulated data.