https://github.com/rensvandeschoot/first-bayesian-inference

This Shiny App is designed to ease its users first contact with Bayesian statistical inference. By "pointing and clicking", the user can analyze the IQ-example as has been used in the easy-to-go introduction to Bayesian inference of van de Schoot et al. (2013).
https://github.com/rensvandeschoot/first-bayesian-inference

Last synced: 3 months ago
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This Shiny App is designed to ease its users first contact with Bayesian statistical inference. By "pointing and clicking", the user can analyze the IQ-example as has been used in the easy-to-go introduction to Bayesian inference of van de Schoot et al. (2013).

• Host: GitHub
• URL: https://github.com/rensvandeschoot/first-bayesian-inference
• Owner: Rensvandeschoot
• License: mit
• Created: 2022-07-06T11:54:06.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-08-04T09:19:39.000Z (about 3 years ago)
• Last Synced: 2024-03-20T09:58:56.056Z (over 1 year ago)
• Language: R
• Size: 181 KB
• Stars: 8
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# First-Bayesian-Inference

## :star: Purpose

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)](https://doi.org/10.1111/cdev.12169). Different prior distributions can be specified, and data with different characteristics can be simulated on the fly. 

## :gem: How can you profit from it?

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](https://github.com/Rensvandeschoot/First-Bayesian-Inference/blob/main/EXERCISE.md) we created. Second, feel free to use this material as a template for your own app. 

## Installation

Download the R-files, open R-studio, install the R-packages and [JAGS](https://mcmc-jags.sourceforge.io/), and run the app.

The Shiny app also runs at a server of [Utrecht University](https://www.rensvandeschoot.com/tutorials/fbi-the-app/). 

[![Overview of FBI Shiny App](fbi_overview.png)](https://utrecht-university.shinyapps.io/bayesian_estimation/)

## Usage

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.

## Reference

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](https://doi.org/10.1111/cdev.12169).

## Contact

For more information about the App, contact [Lion Behrens](https://www.linkedin.com/in/lion-behrens-7173ab102/), [Sonja Winter](https://www.linkedin.com/in/sonjawinter/), or [Rens van de Schoot](https://www.linkedin.com/in/rensvandeschoot/)