The ordered allocation sampler is a Gibbs sampler designed to explore the posterior distribution in nonparametric mixture models. It encompasses both infinite mixtures and finite mixtures with random number of components, and it has be shown to possess mixing properties that pair well with collapsed, or marginal, samplers that integrate out the mixing distribution. The main advantage is that it adapts to mixing priors that do not enjoy tractable predictive structures needed for the implementation of marginal sampling methods. Thus it is as widely applicable as other conditional samplers while enjoying better algorithmic performances. In this paper we provide a modification of the ordered allocation sampler that enhances its performances in a substantial way while easing its implementation. In addition, exploiting the similarity with marginal samplers, we are able to adapt to the new version of the sampler the split-merge moves of Jain and Neal. Simulation studies confirm these findings.

翻译：有序分配采样器是一种Gibbs采样器，专为探索非参数混合模型中的后验分布而设计。它既包含无限混合模型，也包含具有随机分量数的有限混合模型，并且已被证明具有与边缘化采样器（即积分掉混合分布的采样器）相得益彰的混合特性。其主要优势在于，它能适应那些不具备实现边缘采样方法所需可处理预测结构的混合先验。因此，它与其他条件采样器一样具有广泛的适用性，同时享有更优的算法性能。本文提出了一种对有序分配采样器的改进方案，该方案在显著提升其性能的同时简化了其实施过程。此外，通过利用其与边缘采样器的相似性，我们能够将Jain和Neal提出的分裂-合并移动策略适配到新版本的采样器中。模拟研究证实了这些发现。