Applied Bayesian Analytics

This module introduces the foundations of Bayesian inference, including Bayes theorem, advantages of Bayesian models, and their importance in analytics. It also covers major densities and their applications, providing a comprehensive overview of Bayesian statistics and its distinctions from classical statistics.

Skills you'll gain

This module focuses on likelihood theory and estimation, parametrizations and priors, and learning from binary models and Normal Distribution. Students will gain a deeper understanding of how to apply Bayesian analysis to simple statistical models.

Skills you'll gain

This module covers the basics of Monte Carlo integration, Markov chain Monte Carlo, and Gibbs Sampling. Students will learn about these computational techniques essential for Bayesian analysis, especially for complex models where analytical solutions are not feasible.

Skills you'll gain

This module provides examples of Bayesian analytics and introduces R and OpenBUGS for Bayesian analysis. Students will gain practical experience in implementing Bayesian models using these software tools, bridging the gap between theory and application.

Skills you'll gain

This module explores the context for Bayesian regression models, focusing on normal linear regression and logistic regression. Students will learn how to apply Bayesian techniques to these common statistical models, enhancing their ability to analyze real-world data.

Skills you'll gain

This module introduces multilevel models, the concept of exchangeability, and computation in hierarchical models. Students will learn about more advanced Bayesian modeling techniques that can handle complex, nested data structures often encountered in real-world scenarios.

Skills you'll gain