Introduction to Bayesian Analysis (Online)

Programme Structure

Courses include:

• Differentiate the principles of both classical and Bayesian statistical frameworks, and the advantages / disadvantages of each.
• Understand the concept of a probability distribution, including the main established distributions (e.g. the normal distribution) and their parameters.
• Recite the different contributing probability distributions that play a role in Bayes theorem of conditional probabilities.
• Incorporate their prior beliefs into an analysis alongside evidence presented by the collected data.
• Use the results of Bayesian analysis to answer their research question (through prediction, estimation hypothesis testing and credible intervals).
• Have appreciation for the computational complexity of fitting some Bayesian models