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：进行贝叶斯非参数模型推断时，常见的障碍是需要复杂的MCMC算法和/或处理大数据集时的高计算耗时。本文针对富化狄利克雷过程混合模型提出解决方案。我们基于先前开发的EDPM截断近似推导出变分贝叶斯估计器。该变分贝叶斯估计器有两种用途：1) 构建更高效的截断近似；2) 为此高效截断近似对应的分块吉布斯采样器或波利亚瓮采样器提供优质初始值。我们证明了该高效截断近似的精度，并展示了如何在Nimble中简单实现EDPM的分块吉布斯采样器。通过模拟实验验证了近似的有效性，并在真实数据集上进行了实证分析。