The course consists of one course unit:
1. Bayesian Statistics I
In Bayesian inference, parameters are considered to be random variables and any previous knowledge about these parameters is expressed as a probability distribution, the so called a priori distribution. This prior distribution is then updated to a posterior distribution by using Bayes’ theorem to combine it with the observed data which is expressed through the likelihood function. The a posterior distribution, thus, expresses evidence about the parameters after data has been observed.
This course provides an introduction to Bayesian analysis with emphasis on understanding the basic concepts and methods. Simple problems are studied in detail together with an overview and analysis of more complicated real-life problems. The course also provides an introduction to simulation-based computational methods such as the Markov Chain Monte Carlo (MCMC) which are often used in Bayesian inference.
The concepts and topics that will be dealt in more detail in the course are: subjective probabilities, likelihood, a priori and a posteriori distributions, model evaluation, MCMC.