2008
NeurIPS
NeurIPS 2008
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.
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Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Healthcare & Medicine > Research > Bioinformatics
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Interdisciplinary > Science > Bioinformatics
Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian