Bayesian factor analysis with fat-tailed factors and its exact marginal likelihood

Tomohiro Ando

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


The traditional Bayesian factor analysis method is extended. In contrast to the case for previous studies, the matrix variate t-distribution is utilized to provide a prior density on the latent factors. This is a natural extension of the traditional model and yields many advantages. The crucial issue is the selection of the number of factors. The marginal likelihood, constructed by asymptotic and computational approaches, is generally utilized for this problem. However, both theoretical and computational problems have arisen. In this paper, the exact marginal likelihood is derived. It enables us to evaluate posterior model probabilities without inducing the above problems. Monte Carlo experiments were conducted to examine the performance of the proposed Bayesian factor analysis modelling methodology. The simulation results show that the proposed methodology performs well.

Original languageEnglish
Pages (from-to)1717-1726
Number of pages10
JournalJournal of Multivariate Analysis
Issue number8
Publication statusPublished - 2009 Sept
Externally publishedYes


  • Bayesian methods
  • Marginal likelihood
  • Matrix variate t-distribution
  • Model selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Bayesian factor analysis with fat-tailed factors and its exact marginal likelihood'. Together they form a unique fingerprint.

Cite this