A common approach to analyze a covariate-sample count matrix, an element of
which represents how many times a covariate appears in a sample, is to
factorize it under the poisson likelihood. We show its limitation in capturing
the tendency for a covariate present in a sample to both rep
利用负二项分布开发数据增强技术，将计数和混合模型统一到负二项过程中，发展了基本特性和高效的 Gibbs 采样推理，并将 Gamma-NB 过程降低到层次狄利克雷过程的规范形式，展示了其独特的理论性、结构性和计算性优势。构建了多种具有不同共享机制的 NB 过程，并应用于主题建模，与现有算法有关联，展示了推断 NB 分布和概率参数的重要性。