Inference of latent feature models in the Bayesian nonparametric setting is generally difficult, especially in high dimensional settings, because it usually requires proposing features from some prior distribution. In special cases, where the integration is tractable, we can sample new feature assignments according to a predictive likelihood. We present a novel method to accelerate the mixing of latent variable model inference by proposing feature locations based on the data, as opposed to the prior. First, we introduce an accelerated feature proposal mechanism that we show is a valid MCMC algorithm for posterior inference. Next, we propose an approximate inference strategy to perform accelerated inference in parallel. A two-stage algorithm that combines the two approaches provides a computationally attractive method that can quickly reach local convergence to the posterior distribution of our model, while allowing us to exploit parallelization.

翻译：在巴伊西亚非参数设置中,潜伏地物模型的推断一般是困难的,特别是在高维环境中,因为通常需要从某些先前的分布中提出特征。在特殊情况下,在集成可以移动的情况下,我们可以根据预测的可能性抽样新的特征分配。我们提出了一个新颖的方法,通过根据数据而不是先前的数据提出地物位置来加速潜在可变模型推断的混合。首先,我们引入了一个加速地物建议机制,我们显示它是一种有效的后生推论的MCMC算法。接下来,我们提出一种近似推论战略,以平行地进行加速推论。将这两种方法结合起来的两阶段算法提供了一种具有计算吸引力的方法,可以迅速达到与我们模型的后生分布的本地趋同,同时允许我们利用平行法。