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

Learning Bayesian Statistics

NOTE

Prior Sensitivity Reveals Model Strength

Conducting a sensitivity analysis for priors is crucial in refining a modeling workflow. This process involves evaluating the impact of various prior assumptions on the results after the main analysis is completed. By adjusting the informative nature of the priors—either strengthening or weakening them—researchers can test how these changes affect model outputs. This iterative approach allows for a comprehensive understanding of the model's robustness, facilitating a comparison between results obtained with original target priors and those generated under alternative prior scenarios.

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