A common impediment in conducting inference for Bayesian nonparametric models is either the need for complex MCMC algorithms and/or computational run-time for large datasets. We propose solutions here for Enriched Dirichlet process mixtures (EDPM). We derive a variational Bayes estimator based on a previously developed truncation approximation for EDPMs. The variational Bayes estimator can be used in two ways: 1) to develop a more efficient truncation approximation; 2) as good initial values for a blocked Gibbs sampler based on this more efficient truncation approximation or for a polya urn sampler. We derive the accuracy of this more efficient truncation approximation and demonstrate how this allows for simple implementation of a blocked Gibbs Sampler EDPMs in Nimble. We confirm the validity of the approximations by simulations and illustrate on a real data set.

翻译：贝叶斯非参数模型推断中常见的障碍包括需要复杂的MCMC算法和/或处理大规模数据集时的计算耗时问题。本文针对增强狄利克雷过程混合模型提出解决方案。基于先前开发的EDPM截断近似方法，我们推导出相应的变分贝叶斯估计量。该估计量可通过两种方式应用：1）构建更高效的截断近似方法；2）为基于该高效截断近似的分块吉布斯采样器或波利亚瓮采样器提供优质初始值。我们推导了该高效截断近似的精度，并演示了如何在Nimble中实现分块吉布斯采样器EDPM的简易部署。通过模拟实验验证了近似方法的有效性，并在真实数据集上进行了应用展示。