The Infinite Hierarchical Factor Regression Model

Abstract

We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We apply this model to two problems (factor analysis and factor regression) in gene-expression data analysis.

References in corpus (1)

• Bayesian Agglomerative Clustering with Coalescents

Cited by in corpus (7)

• Nonparametric Bayesian sparse factor models with application to gene expression modeling
• Bayesian Group Factor Analysis
• Sparse Linear Identifiable Multivariate Modeling
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• Efficient hierarchical clustering for continuous data
• Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction
• Semiparametric Functional Factor Models with Bayesian Rank Selection