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Bayes factors for mixed effects models are complex. Experts disagree on best practices, highlighting the need to understand model assumptions for robust Bayesian model comparison.

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Area of Science:

  • Statistics
  • Bayesian inference
  • Mixed effects modeling

Background:

  • Bayes factors are used for mixed effects model comparison.
  • Open questions exist regarding aggregation, measurement error, priors, and interactions.
  • Expert commentaries revealed significant disagreement on best practices.

Purpose of the Study:

  • To provide a perspective on expert commentaries regarding Bayes factors for mixed effects models.
  • To highlight key topics for further discussion in mixed effects model comparison.
  • To emphasize the importance of understanding model assumptions.

Main Methods:

  • Review and synthesis of expert commentaries on a previous work.
  • Discussion of open questions in Bayesian mixed effects model comparison.
  • Elaboration on the impact of aggregation, measurement error, priors, and interactions.

Main Results:

  • Expert opinions varied widely on optimal methods for mixed effects model comparison.
  • The complexity of mixed effects models necessitates careful consideration of underlying assumptions.
  • Further discussion is needed to establish best practices.

Conclusions:

  • Understanding specific model assumptions is crucial for effective Bayesian mixed model comparison.
  • The intricate nature of mixed effects models requires nuanced approaches.
  • Continued dialogue among experts is essential for advancing the field.