Advanced Bayesian Data Analysis Using R is part two of the Bayesian Data Analysis in R professional certificate.
This course is directed at people who are already familiar with the
fundamentals of Bayesian inference. It explores further the concepts,
methods, and algorithms introduced in the part one (Introductory
Bayesian Data Analysis Using R).
The course places mixed effects regression models useful for
experiments with repeated measures or additional hierarchy often
encountered in biostatistics, ecology and health sciences among others
within the Bayesian context. It takes a closer look at the Markov Chain
Monte Carlo (MCMC) algorithms, why they work and how to implement them
in the R programming language. Convergence assessment and visualisation
of the results are discussed in some detail. The course also explores
Bayesian model averaging, often used in machine learning, all within the
context of practical examples.
Finally, we discuss different kinds of missing data, and the Bayesian methods of dealing with such situations.
Prior facility in basic algebra and calculus as well as programming in R is highly recommended.
