Episode
#109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter
- Podcast
- Learning Bayesian Statistics
- Published
- Jun 25, 2024
- Duration seconds
- 4250
- Processing state
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Summary
Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! My Intuitive Bayes Online Courses 1:1 Mentorship with me Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Visit our Patreon page to unlock exclusive Bayesian swag ;) 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! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robi…