  Stochastic variational inference (SVI) is emerging as the most promising
candidate for scaling inference in Bayesian probabilistic models to large
datasets. However, the performance of these methods has been assessed primarily
in the context of Bayesian topic models, particularly latent Dirichlet
allocation (LDA). Deriving several new algorithms, and using synthetic, image
and genomic datasets, we investigate whether the understanding gleaned from LDA
applies in the setting of sparse latent factor models, specifically beta
process factor analysis (BPFA). We demonstrate that the big picture is
consistent: using Gibbs sampling within SVI to maintain certain posterior
dependencies is extremely effective. However, we find that different posterior
dependencies are important in BPFA relative to LDA. Particularly,
approximations able to model intra-local variable dependence perform best.
