Open Access
Translator Disclaimer
June 2011 Nonparametric Bayesian sparse factor models with application to gene expression modeling
David Knowles, Zoubin Ghahramani
Ann. Appl. Stat. 5(2B): 1534-1552 (June 2011). DOI: 10.1214/10-AOAS435


A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indian Buffet Process (IBP) is used as a prior on 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.


Download Citation

David Knowles. Zoubin Ghahramani. "Nonparametric Bayesian sparse factor models with application to gene expression modeling." Ann. Appl. Stat. 5 (2B) 1534 - 1552, June 2011.


Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1223.62013
MathSciNet: MR2849785
Digital Object Identifier: 10.1214/10-AOAS435

Keywords: factor analysis , Indian buffet process , Markov chain Monte Carlo , nonparametric Bayes , Sparsity

Rights: Copyright © 2011 Institute of Mathematical Statistics


Vol.5 • No. 2B • June 2011
Back to Top