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Bayes Factors for Mixed Models: a Discussion

  • Johnny van Doorn
  • , Julia M. Haaf
  • , Angelika M. Stefan
  • , Eric-Jan Wagenmakers
  • , Gregory Edward Cox
  • , Clintin P. Davis-Stober
  • , Andrew Heathcote
  • , Daniel W. Heck
  • , Michael Kalish
  • , David Kellen
  • , Dora Matzke
  • , Richard D. Morey
  • , Bruno Nicenboim
  • , Don van Ravenzwaaij
  • , Jeffrey N. Rouder
  • , Daniel J. Schad
  • , Richard M. Shiffrin
  • , Henrik Singmann
  • , Shravan Vasishth
  • , João Veríssimo
  • Florence Bockting, Suyog Chandramouli, John C. Dunn, Quentin F. Gronau, Maximilian Linde, Sara D. McMullin, Danielle Navarro, Martin Schnuerch, Himanshu Yadav, Frederik Aust

    Research output: Contribution to journalArticleScientificpeer-review

    Abstract

    van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison.
    Original languageEnglish
    Pages (from-to)140-158
    Number of pages19
    JournalComputational Brain & Behavior
    Volume6
    DOIs
    Publication statusPublished - Mar 2023

    Keywords

    • Bayes factors
    • Mixed effects
    • Mixed models
    • Random effects

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