Normalised latent measure factor models
提出一种在贝叶斯非参数框架下建模和比较概率分布的方法，通过潜在度量线性组合和黎曼优化实现可识别推断，在模拟和真实数据（学生成绩、加州个人收入）上验证了有效性。
Abstract We propose a methodology for modelling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalised random measures, we consider a prior distribution for a collection of discrete random measures where each measure is a linear combination of a set of latent measures, interpretable as characteristic traits shared by different distributions, with positive random weights. The model is nonidentified and a method for postprocessing posterior samples to achieve identified inference is developed. This uses Riemannian optimisation to solve a nontrivial optimisation problem over a Lie group of matrices. The effectiveness of our approach is validated on simulated data and in two applications to two real-world data sets: school student test scores and personal incomes in California. Our approach leads to interesting insights for populations and easily interpretable posterior inference.