The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian
nonparametric modeling, and is widely used in tasks such as density estimation,
natural language processing, and time series modeling. Although MCMC inference
methods for the DP often provide a gold standard in terms asymptotic accuracy,
they can be computationally expensive and are not obviously parallelizable. We
propose a reparameterization of the Dirichlet process that induces conditional
independencies between the atoms that form the random measure. This conditional
independence enables many of the Markov chain transition operators for DP
inference to be simulated in parallel across multiple cores. Applied to mixture
modeling, our approach enables the Dirichlet process to simultaneously learn
clusters that describe the data and superclusters that define the granularity
of parallelization. Unlike previous approaches, our technique does not require
alteration of the model and leaves the true posterior distribution invariant.
It also naturally lends itself to a distributed software implementation in
terms of Map-Reduce, which we test in cluster configurations of over 50
machines and 100 cores. We present experiments exploring the parallel
efficiency and convergence properties of our approach on both synthetic and
real-world data, including runs on 1MM data vectors in 256 dimensions.

本文介绍一种新型的 DP 重新参数化方法，该方法在聚类分析中广泛使用，能够实现 DP 的并行学习，从而提高了学习速度和效率，同时该方法不需要改变模型并且能够保持标准后验分布不变。