Simultaneous parameter estimation and variable selection via the logit-normal continuous analogue of the spike-and-slab prior

Research output: Contribution to journalArticlepeer-review

External organisations

  • Institute of Microbiology and Infection, College of Medical and Dental Science, University of Birmingham, Birmingham, UK.
  • Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
  • School of Mathematics, Watson Building; University of Birmingham; Edgbaston Birmingham B15 2TT UK


We introduce a Bayesian prior distribution, the logit-normal continuous analogue of the spike-and-slab, which enables flexible parameter estimation and variable/model selection in a variety of settings. We demonstrate its use and efficacy in three case studies—a simulation study and two studies on real biological data from the fields of metabolomics and genomics. The prior allows the use of classical statistical models, which are easily interpretable and well known to applied scientists, but performs comparably to common machine learning methods in terms of generalizability to previously unseen data.


Original languageEnglish
Article number20180572
Pages (from-to)20180572
JournalJournal of The Royal Society Interface
Issue number150
Early online date2 Jan 2019
Publication statusPublished - 31 Jan 2019


  • Bayesian, Shrinkage, Spike-and-slab, Variable selection