Learning

Computational Bayesian Methods: MCMC and Applications

Probability Theory Foundations → Capstone Project: Full Bayesian Analysis

This path equips graduate students with the knowledge and skills to apply computational Bayesian methods, focusing on Markov Chain Monte Carlo (MCMC) techniques such as the Metropolis-Hastings algorithm and Gibbs sampling. It covers the theoretical foundations, practical implementation, and real-world applications, culminating in a capstone project.