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A blocked Gibbs sampler for NGG-mixture models via a priori truncation

机译：通过先验截断得到的用于NGG混合物模型的封闭式Gibbs采样器

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摘要

We define a new class of random probability measures, approximating the well-known normalized generalized gamma (NGG) process. Our new process is defined from the representation of NGG processes as discrete measures where the weights are obtained by normalization of the jumps of Poisson processes and the support consists of independent identically distributed location points, however considering only jumps larger than a threshold epsilon. Therefore, the number of jumps of the new process, called epsilon-NGG process, is a.s. finite. A prior distribution for epsilon can be elicited. We assume such a process as the mixing measure in a mixture model for density and cluster estimation, and build an efficient Gibbs sampler scheme to simulate from the posterior. Finally, we discuss applications and performance of the model to two popular datasets, as well as comparison with competitor algorithms, the slice sampler and a posteriori truncation.
机译：我们定义了一类新的随机概率测度，近似于众所周知的归一化广义伽玛（NGG）过程。我们的新过程是通过将NGG过程表示为离散量度而定义的，其中权重是通过对泊松过程的跃迁进行归一化而获得的，并且支撑由独立的均匀分布的位置点组成，但是仅考虑大于阈值epsilon的跃迁。因此，称为epsilon-NGG过程的新过程的跳跃数为a.s。有限。可以得出ε的先前分布。我们假设此过程为混合模型中用于密度和聚类估计的混合度量，并构建了一个有效的Gibbs采样器方案以从后验模拟。最后，我们讨论了该模型在两个流行数据集上的应用和性能，以及与竞争算法，切片采样器和后验截断的比较。

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