论文标题
分布式的，部分折叠的MCMC用于贝叶斯非参数
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
论文作者
论文摘要
贝叶斯非参数（BNP）模型提供了优雅的方法，可在数据集中发现潜在的潜在特征，但是在此类模型中的推断可能很慢。我们利用这样一个事实，即完全随机的度量（例如Dirichlet过程）和Beta-Bernoulli过程可以表示为分解为独立的子测量。 We use this decomposition to partition the latent measure into a finite measure containing only instantiated components, and an infinite measure containing all other components.然后，我们为两个组件选择不同的推理算法：未填充的采样器在有限的措施上充分混合，而折叠的采样器在无限的无限且稀疏的尾巴上充分混合。所得的混合算法可以应用于广泛的模型，并且可以轻松分布以允许可扩展的推断而无需牺牲渐近收敛保证。
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the fact that completely random measures, which commonly used models like the Dirichlet process and the beta-Bernoulli process can be expressed as, are decomposable into independent sub-measures. We use this decomposition to partition the latent measure into a finite measure containing only instantiated components, and an infinite measure containing all other components. We then select different inference algorithms for the two components: uncollapsed samplers mix well on the finite measure, while collapsed samplers mix well on the infinite, sparsely occupied tail. The resulting hybrid algorithm can be applied to a wide class of models, and can be easily distributed to allow scalable inference without sacrificing asymptotic convergence guarantees.
