Welcome to my repository on Bayesian Data Science. This is a collection of study notes and practical examples developed as part of my MSc Dissertation at the University of St Andrews.
The content is written in Julia and presented as a series of Jupyter Notebooks. These notebooks explore key topics in Bayesian inference, probabilistic modeling, and related computational techniques.
- 📘 Introductory notes on Bayesian statistics
- 🧮 Examples of Bayesian models implemented in Julia : Bayesian Logistic Regression, Fixed Basis Expansion
- 📊 Visualizations and inference techniques using probabilistic programming : Variational Inference, Gibbs Sampling
To run the notebooks, you’ll need:
- Julia (>= 1.11.3)
- Jupyter (via
IJulia.jl) - Additional Julia packages listed in each notebook
Feel free to use or adapt this material for academic and personal projects. Please credit the original source when applicable.