Generalized cumulative shrinkage process priors with applications to sparse Bayesian factor analysis

Sylvia Frühwirth-Schnatter*

*Corresponding author for this work

Publication: Scientific journalJournal articlepeer-review


The paper discusses shrinkage priors which impose increasing shrinkage in a sequence of parameters. We review the cumulative shrinkage process (CUSP) prior of Legramanti et al. (Legramanti et al. 2020 Biometrika 107, 745-752. (doi:10.1093/biomet/asaa008)), which is a spike-and-slab shrinkage prior where the spike probability is stochastically increasing and constructed from the stick-breaking representation of a Dirichlet process prior. As a first contribution, this CUSP prior is extended by involving arbitrary stick-breaking representations arising from beta distributions. As a second contribution, we prove that exchangeable spike-and-slab priors, which are popular and widely used in sparse Bayesian factor analysis, can be represented as a finite generalized CUSP prior, which is easily obtained from the decreasing order statistics of the slab probabilities. Hence, exchangeable spike-and-slab shrinkage priors imply increasing shrinkage as the column index in the loading matrix increases, without imposing explicit order constraints on the slab probabilities. An application to sparse Bayesian factor analysis illustrates the usefulness of the findings of this paper. A new exchangeable spike-and-slab shrinkage prior based on the triple gamma prior of Cadonna et al. (Cadonna et al. 2020 Econometrics 8, 20. (doi:10.3390/econometrics8020020)) is introduced and shown to be helpful for estimating the unknown number of factors in a simulation study. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Original languageEnglish
Article number20220148
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s).


  • Bayesian inference
  • exchangeability
  • factor analysis
  • factor dimension
  • Markov chain Monte Carlo
  • shrinkage priors

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