The aim of this course is to introduce students to the Bayesian statistical modelling and inference andto the related computational strategies and algorithms. The course starts with the treatment of simple models, such as those based on normal and binomial distributions. Some advanced models will be treated, including hierarchical models, linear regression models and generalized linear models. Bayesian computational methods (MCMC), including Gibbs sampler and Metropolis-Hastings algorithms, are presented with an emphasis on the issues related to their implementation and monitoring of convergence.

CILO-1: Apply the Bayesian statistical methods to design and develop computational strategies and algorithms. CILO-2: Apply Bayesian computational methods to solve real-world problems in various fields, including data science, machine learning and applied statistics. CILO-3: Utilize the Markov Chain Monte Carlo, Gibbs sampler and Metropolis-Hastings algorithms to solve problems in Bayesian statistics.