Bayesian evaluation of informative hypotheses for multiple populations

Herbert Hoijtink*, Xin Gu, Joris Mulder

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
23 Downloads (Pure)


The software package Bain can be used for the evaluation of informative hypotheses with respect to the parameters of a wide range of statistical models. For pairs of hypotheses the support in the data is quantified using the approximate adjusted fractional Bayes factor (BF). Currently, the data have to come from one population or have to consist of samples of equal size obtained from multiple populations. If samples of unequal size are obtained from multiple populations, the BF can be shown to be inconsistent. This paper examines how the approach implemented in Bain can be generalized such that multiple‐population data can properly be processed. The resulting multiple‐population approximate adjusted fractional Bayes factor is implemented in the R package Bain.
Original languageEnglish
Pages (from-to)219-243
JournalBritish Journal of Mathematical and Statistical Psychology
Issue number2
Publication statusPublished - 2019


  • Bain
  • Bayes factor
  • informative hypotheses
  • multiple populations

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