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Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings.

Blake Hansen1, Alejandra Avalos-Pacheco2, Massimiliano Russo3

  • 1Department of Biostatistics, Brown University.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|March 31, 2025
PubMed
Summary
This summary is machine-generated.

New variational inference algorithms offer faster, scalable analysis for Bayesian factor models. These methods efficiently handle high-dimensional data, outperforming traditional Markov Chain Monte Carlo (MCMC) approaches in speed and memory usage.

Keywords:
Factor AnalysisMulti-StudyShrinkage priorVariational Bayes

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Area of Science:

  • Statistics
  • Computational Biology
  • Bioinformatics

Background:

  • Factor models are crucial for analyzing high-dimensional data in single and multi-study contexts.
  • Bayesian inference for these models typically uses Markov Chain Monte Carlo (MCMC), which struggles with scalability due to increasing data complexity.

Purpose of the Study:

  • To develop novel variational inference algorithms for Bayesian latent factor models.
  • To address the computational limitations of MCMC methods in high-dimensional settings.

Main Methods:

  • Proposed new variational inference algorithms utilizing a multiplicative gamma process shrinkage prior.
  • Compared the performance and accuracy of the new algorithms against MCMC implementations.

Main Results:

  • The proposed variational inference algorithms provide fast approximate inference.
  • These methods require significantly less time and memory compared to MCMC.
  • Achieved comparable accuracy in characterizing the data covariance matrix.
  • Demonstrated utility in analyzing high-dimensional, multi-study gene expression data from ovarian cancers.

Conclusions:

  • The developed variational inference approaches offer efficient and scalable solutions for factor models.
  • Facilitates the application of factor models in high-dimensional data analysis.
  • An R package, VIMSFA, is available for implementing these methods.