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

TL;DR

This paper introduces an infinite hierarchical non-parametric Bayesian model with deep learning structure for extracting hidden factors in data, applicable to wireless networks, and demonstrates its effectiveness through simulations.

Contribution

It proposes a novel infinite hierarchical Bayesian model with deep learning features, capable of handling unknown and potentially infinite hidden factors and layers.

Findings

Model accurately fits simulated data structure

The approach effectively infers model structure using Metropolis-Hastings

Demonstrates potential in wireless network applications

Abstract

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