Generalised data augmentation and posterior inferences

Gavin J. Gibson, George Streftaris, Stan Zachary

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


This paper explores the use of data augmentation in settings beyond the standard Bayesian one. In particular, we show that, after proposing an appropriate generalised data-augmentation principle, it is possible to extend the range of sampling situations in which fiducial methods can be applied by constructing Markov chains whose stationary distributions represent valid posterior inferences on model parameters. Some properties of these chains are presented and a number of open questions are discussed. We also use the approach to draw out connections between classical and Bayesian approaches in some standard settings. © 2010 Elsevier B.V.

Original languageEnglish
Pages (from-to)156-171
Number of pages16
JournalJournal of Statistical Planning and Inference
Issue number1
Publication statusPublished - Jan 2011


  • Bayesian inference
  • Data augmentation
  • Fiducial inference
  • Markov chain methods


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