The goal of this course is to develop a solid theoretical understanding of Bayesian statistics, with an eye towards its applications.
The course will start by covering the necessary basics such as probability, probability distributions, independence etc. before moving on to looking at the theory behind Bayesian inference and simulation methods such as Monte Carlo Markov Chains. Finally, and time permitting, we will look at some practical examples of how to perform Bayesian inference on a computer using standard statistics libraries.