You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Towards Bayesian Deep Learning: A Survey 
Chapter Title:  Deep Poisson Factor Analysis with Restricted Boltzmann Machine 
1. Abstract of Cited Paper (BIB001):  A new framework for topic modeling is developed, based on deep graphical models, where interactions between topics are inferred through deep latent binary hierarchies. The proposed multi-layer model employs a deep sigmoid belief network or restricted Boltzmann machine, the bottom binary layer of which selects topics for use in a Poisson factor analysis model. Under this setting, topics live on the bottom layer of the model, while the deep specification serves as a flexible prior for revealing topic structure. Scalable inference algorithms are derived by applying Bayesian conditional density filtering algorithm, in addition to extending recently proposed work on stochastic gradient thermostats. Experimental results on several corpora show that the proposed approach readily handles very large collections of text documents, infers structured topic representations, and obtains superior test perplexities when compared with related models.

Literature Review:
Similar to the deep PFA above, the restricted Boltzmann machine (RBM)  can be used in place of SBN BIB002 . If RBM is used, Equation BIB001 and (20) would be defined using the energy  : For the learning, similar algorithms as the deep PFA with SBN can be used. Specifically, the sampling process would alternate between {{φ k }, {γ k }, γ 0 } and {{W (l) }, {c (l) }}. For the former, similar conditional density as the SBN-based DPFA is used. For the latter, they use the contrastive divergence algorithm.