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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents

ID: 461810.0, MPI für biologische Kybernetik / Biologische Kybernetik
Inference algorithms and learning theory for Bayesian sparse factor analysis
Authors:Rattray, M.; Stegle, O.; Sharp, K.; Winn, J.
Editors:Inoue, M.; Ishii, S.; Kabashima, Y.; Okada, M.
Date of Publication (YYYY-MM-DD):2009-09
Title of Proceedings:Proceedings of the International Workshop on Statistical-Mechanical Informatics 2009 (IW-SMI 2009)
Start Page:1
End Page:10
Physical Description:10
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
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