An iterative Monte Carlo method for nonconjugate Bayesian analysis

Brad Carlin, Alan E. Gelfand

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


The Gibbs sampler has been proposed as a general method for Bayesian calculation in Gelfand and Smith (1990). However, the predominance of experience to date resides in applications assuming conjugacy where implementation is reasonably straightforward. This paper describes a tailored approximate rejection method approach for implementation of the Gibbs sampler when nonconjugate structure is present. Several challenging applications are presented for illustration.

Original languageEnglish (US)
Pages (from-to)119-128
Number of pages10
JournalStatistics and Computing
Issue number2
StatePublished - Dec 1 1991


  • Bayesian inference
  • Gibbs sampler
  • hierarchical models
  • logistic regression
  • nonlinear models
  • rejection method


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