This Shiny App is designed to ease its users first contact with Bayesian statistical inference, investigate the effect of different prior distributions on the posterior result, and understand prior-data conflict. By "pointing and clicking", the user can analyze the IQ-example that has been used in the easy-to-go introduction to Bayesian inference of van de Schoot et al. (2013). Different prior distributions can be specified, and data with different characteristics can be simulated on the fly.
First of all, this app might be a useful tool for your teaching if you would like to familiarize your students with the basic logic of Bayesian inference, see also the exercise we created. Second, feel free to use this material as a template for your own app.
Download the R-files, open R-studio, install the R-packages and JAGS, and run the app.
The Shiny app also runs at a server of Utrecht University.
Step 1: Open the Shiny App.
Step 2: Choose a type of distribution (i.e., uniform, truncated Normal) for the prior and fill in values for the hyperparameters.
Step 3: Generate data.
Step 4: Let the software (analytically or via sampling using RJags) generate the posterior distribution.
Van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & Aken, M. A. (2014). A gentle introduction to Bayesian analysis: applications to developmental research. Child development, 85(3), 842-860. DOI: 10.1111/cdev.12169.
For more information about the App, contact Lion Behrens, Sonja Winter, or Rens van de Schoot