[Please note that this is a two-part training event and students are expected to attend both parts: Monday 23 and Wednesday 25 June, 2-5pm]
led by Dr Katherine Auty, University of Cambridge
This course aims to introduce students to the fundamental concepts of Bayesian theory. It provides a foundational understanding of the principles, methods, and applications of Bayesian statistics, which offers a robust framework for data analysis and decision-making by incorporating prior knowledge and uncertainty in a structured way. The course will cover essential topics such as Bayes’ theorem, prior and posterior distributions, likelihood functions, and the key distinctions between Bayesian and frequentist approaches. Students will learn how to build and estimate statistical models, update beliefs with new data, and make decisions based on the posterior probabilities derived through Bayesian inference. By the end of the course, students will have the skills to conduct Bayesian data analysis, interpret the results, and apply Bayesian methods in a range of practical situations.
Attendees should have basic statistical knowledge including regression analyses, and should have R downloaded and available on their devices. Video recordings will be available to review prior to the session.