Accelerated Inference for Latent Variable Models

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 could sample feature assignments according to a predictive likelihood. However, this still may not be efficient in high dimensions. We present a novel method to accelerate the mixing of latent variable model inference by proposing feature locations from the data, as opposed to the prior. This sampling method is efficient for proper mixing of the Markov chain Monte Carlo sampler, computationally attractive because this method can be performed in parallel, and is theoretically guaranteed to converge to the posterior distribution as its limiting distribution.