Tutorial for Bayesian Factor Models

Peter Dunson; Ciprian M. Crainiceanu

Tutorial for Bayesian Factor Models

统计计算 2026-07-13 v1 统计方法学

作者: Peter Dunson , Ciprian M. Crainiceanu

摘要

Bayesian Factor Models (BFM) are well-established models that decompose the observed variability in a set of mean-zero, independent, and uncorrelated factors (random effects). While Factor Analysis (FA) was introduced in 1904 by Spearman, there has been renewed interest in inferential and computational methods that can adapt to large and complex modern data sets that are now routinely collected in a variety of applications. We provide reproducible, harmonized, and fast software for a variety of recent BFMs that allows the direct comparison of methods and provides a one-stop tutorial for the BFMs and their implementation. We neither endorse nor recommend any of the methods for a particular application; we simply provide a previously unavailable harmonized and reproducible common platform for BFMs. The accompanying factorverse R package is available at https://github.com/peterdunson/factorverse.

引用

@article{arxiv.2607.11819,
  title  = {Tutorial for Bayesian Factor Models},
  author = {Peter Dunson and Ciprian M. Crainiceanu},
  journal= {arXiv preprint arXiv:2607.11819},
  year   = {2026}
}

备注

Code available at https://github.com/peterdunson/factorverse