DiscoverLearning Bayesian Statistics#109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter
#109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter

#109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter

Update: 2024-06-25
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Takeaways

  • Bayesian methods align better with researchers' intuitive understanding of research questions and provide more tools to evaluate and understand models.
  • Prior sensitivity analysis is crucial for understanding the robustness of findings to changes in priors and helps in contextualizing research findings.
  • Bayesian methods offer an elegant and efficient way to handle missing data in longitudinal studies, providing more flexibility and information for researchers.
  • Fit indices in Bayesian model selection are effective in detecting underfitting but may struggle to detect overfitting, highlighting the need for caution in model complexity.
  • Bayesian methods have the potential to revolutionize educational research by addressing the challenges of small samples, complex nesting structures, and longitudinal data. 
  • Posterior predictive checks are valuable for model evaluation and selection.

Chapters

00:00 The Power and Importance of Priors

09:29 Updating Beliefs and Choosing Reasonable Priors

16:08 Assessing Robustness with Prior Sensitivity Analysis

34:53 Aligning Bayesian Methods with Researchers' Thinking

37:10 Detecting Overfitting in SEM

43:48 Evaluating Model Fit with Posterior Predictive Checks

47:44 Teaching Bayesian Methods

54:07 Future Developments in Bayesian Statistics

Thank you to my Patrons for making this episode possible!

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Transcript

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#109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter

#109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter

Alexandre Andorra