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The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App
Sarah Depaoli1, Sonja D Winter1, Marieke Visser1
1Department of Psychological Sciences, University of California, Merced, Merced, CA, United States.
Frontiers in Psychology
|December 16, 2020
Summary
This study introduces an interactive Shiny App to teach prior sensitivity analysis in Bayesian estimation. The app helps users understand how different priors impact model estimates, crucial for reliable results.
Area of Science:
- Statistics
- Computational Statistics
Background:
- Prior sensitivity analysis is vital in Bayesian estimation.
- Examining prior distributions, even diffuse ones, is crucial for robust results.
Purpose of the Study:
- To introduce an interactive Shiny App for teaching prior sensitivity analysis.
- To demonstrate the impact of various priors on Bayesian model estimates.
- To guide users in conducting, comparing, and reporting prior sensitivity analyses.
Main Methods:
- A simulation study was conducted to illustrate prior impact.
- An interactive Shiny App was developed for exploring prior sensitivity.
- A multiple regression model example is detailed for user comprehension.
Main Results:
- Simulation findings underscore the necessity of prior sensitivity analysis.
- The Shiny App enables interactive exploration of prior effects on empirical data.
- The study provides a framework for setting up and interpreting sensitivity analyses.
Conclusions:
- Prior sensitivity analysis is essential for all types of priors in Bayesian methods.
- The developed Shiny App facilitates understanding and application of these analyses for a broad audience.
- This tool enhances the learning curve for Bayesian estimation and prior evaluation.
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