## Nonparametric Bayesian sparse factor models with application to gene expression modeling.(English)Zbl 1223.62013

Summary: 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.

### MSC:

 62F15 Bayesian inference 62H25 Factor analysis and principal components; correspondence analysis 62G99 Nonparametric inference 65C40 Numerical analysis or methods applied to Markov chains

### Keywords:

Markov chain Monte Carlo; Indian buffet process

PMA
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### References:

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