ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2025
Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- ISPAT-3D: Spatially Varying Conditional Volumetric Network Estimation for 3D Tumor Imaging.Research square · 2026Article
- Robust discovery of mutational signatures using power posteriors.PLoS computational biology · 2026Article
- A Tutorial on Bayesian Multi-Study Factor Analysis With Applications in Nutrition and Genomics.Statistics in medicine · 2026Article
- ISPAT-3D: Spatially Varying Conditional Volumetric Network Estimation for 3D Tumor Imaging.bioRxiv : the preprint server for biology · 2026Article
- Informed spatially aware patterns for multiplexed immunofluorescence data.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Factors models are commonly used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte Carlo (MCMC) methods, which scale poorly as the number of studies, observations, or measured variables increase. To address this issue, we propose new variational inference algorithms to approximate the posterior distribution of Bayesian latent factor models using the multiplicative gamma process shrinkage prior. The proposed algorithms provide fast approximate inference at a fraction of the time and memory of MCMC-based implementations while maintaining comparable accuracy in characterizing the data covariance matrix. We conduct extensive simulations to evaluate our proposed algorithms and show their utility in estimating the model for high-dimensional multi-study gene expression data in ovarian cancers. Overall, our proposed approaches enable more efficient and scalable inference for factor models, facilitating their use in high-dimensional settings. An R package VIMSFA implementing our methods is available on GitHub (github.com/blhansen/VI-MSFA).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.