  A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where
observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$,
of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian
Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity
and to allow the number of latent features to be inferred. The model's utility
for modeling gene expression data is investigated using randomly generated data
sets based on a known sparse connectivity matrix for E. Coli, and on three
biological data sets of increasing complexity.
