Bayesian Inference
This project demonstrates advanced skills in Bayesian hierarchical modelling, data visualisation, and statistical inference using R and JAGS. It showcases the ability to preprocess real-world epidemiological data, implement and diagnose MCMC simulations using Gibbs sampling, and interpret convergence diagnostics (e.g., traceplots, Gelman-Rubin statistics, and autocorrelation). The work highlights proficiency in quantifying uncertainty, leveraging shrinkage effects, and adapting models for updated demographic data. It also reflects strong analytical thinking in comparing posterior estimates across population strata, critically evaluating prior influence, and effectively communicating results through both code and narrative-skills essential for rigorous, reproducible, and insightful statistical analysis.