Computational Bayesian Statistics for Data Science

This course equips learners with the theoretical knowledge and computational skills needed to implement modern Bayesian statistical methods in real-world settings. By completing the course, learners will be able to build and fit Bayesian models, apply computational algorithms for posterior inference, and interpret uncertainty in complex data analysis problems. Topics include maximum a posteriori (MAP) estimation, rejection sampling, and Markov chain Monte Carlo (MCMC) methods such as the Gibbs sampler and Metropolis-Hastings algorithms. Learners will also gain hands-on experience using Stan, o