Bayesian Statistics Ioslides

Content Overview

The presentation covers essential topics in Bayesian statistics, including Bayes' Theorem, data visualization using ggplot2 and plotly with the "US Arrests" dataset, and the application of Bayes' Theorem to predict future murders in US states. The presentation includes interactive visualizations and code snippets to enhance understanding.

Presentation Structure

What is Bayesian Statistics?: Introduction to Bayesian statistics and its role in managing uncertainty. Bayes' Theorem: Explanation of the fundamental equation of Bayesian statistics. Dataset US Arrests: Exploration of the dataset through code and visualizations. US Arrests for Murder and Assault: Data visualizations to gain insights into murder and assault instances across different states. Comparison of UrbanPop against Murder, Rape, and Assault: 3D scatter plot showcasing correlations. Using Bayes Theorem to predict future murders in US States: Application of Bayes' Theorem to predict future murders. Code for the Bayes Theorem to plot the USArrests: Implementation of Bayes' Theorem in R code. Plot for the Bayes Theorem: Visualization of the results through a histogram.

Styling

The presentation features custom styling to enhance visual appeal, including a distinctive color palette and font choices.

Dependencies

The code utilizes R packages such as ggplot2, datasets, and plotly. For the Bayesian analysis, the use of "rstan" or "brms" packages is recommended, with Stan as the programming language for Bayesian data analysis.

Author

Grina Hwang

How to Use

To get started:

1. Clone or download the repository to your local machine.
2. Install the necessary R packages using the provided setup instructions.
3. Open the R Markdown file or individual R scripts to view and run the code.

Additional Resources

For further assistance or questions related to DAT 301 coursework, please reach out to your course instructor or refer to the course materials.

Best of luck with your studies!