A Bayesian approach to machine learning is attractive when we need to quantify uncertainty, deal with missing observations, when samples are scarce, or when the data is sparse. All of these commonly apply when analysing healthcare data. To address these analytical requirements, we propose a deep generative model for multinomial count data where both the weights and hidden units of the network are Dirichlet distributed. A Gibbs sampling procedure is formulated that takes advantage of a series of augmentation relations, analogous to the Zhou--Cong--Chen model. We apply the model on small handwritten digits, and a large experimental dataset of DNA mutations in cancer, and we show how the model is able to extract biologically meaningful meta-signatures in a fully data-driven way.

翻译：当我们需要量化不确定性、处理缺失观测值、样本稀缺或数据稀疏时，贝叶斯方法在机器学习中具有吸引力。这些情况在分析医疗数据时经常出现。为满足这些分析需求，我们提出了一种针对多项计数数据的深度生成模型，其中网络的权重和隐藏单元均采用狄利克雷分布。我们构建了一种吉布斯采样程序，该程序利用一系列数据增强关系，类似于Zhou-Cong-Chen模型。我们将该模型应用于小型手写数字数据集以及大规模癌症DNA突变实验数据集，并展示了该模型如何以完全数据驱动的方式提取具有生物学意义的元特征。