TL;DR借助经验变分推理技术,本文提出一种基于贝叶斯非参数方法的隐变量模型——population random measure embedding (PRME),并应用于高维数据的建模与推断。
Abstract
The likelihood model of many high dimensional data $X_n$ can be expressed as $p(X_n|Z_n,\theta)$, where $\theta\mathrel{\mathop:}=(\theta_k)_{k\in[K]}$ is a collection of hidden features shared across objects (in