  Hierarchical beta process has found interesting applications in recent years.
In this paper we present a modified hierarchical beta process prior with
applications to hierarchical modeling of multiple data sources. The novel use
of the prior over a hierarchical factor model allows factors to be shared
across different sources. We derive a slice sampler for this model, enabling
tractable inference even when the likelihood and the prior over parameters are
non-conjugate. This allows the application of the model in much wider contexts
without restrictions. We present two different data generative models a linear
GaussianGaussian model for real valued data and a linear Poisson-gamma model
for count data. Encouraging transfer learning results are shown for two real
world applications text modeling and content based image retrieval.
