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

Learning Bayesian Statistics

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Unlocking Bayesian Methods in Structure Modeling

This chapter explores the guest's journey from developmental psychology to Bayesian statistics, focusing on the role of priors in structural equation modeling (SEM). It highlights the challenges researchers face in prior selection, sensitivity analysis, and the importance of prior predictive checks as essential steps in the modeling process. The conversation reveals how advancements in software and methods like simulation-based calibration are enhancing the accessibility and reliability of Bayesian analysis.

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