Bayesian Statistics

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The purpose of this course is to familiarise students with the basic concepts of Bayesian theory. It is designed to provide an introduction to the principles, methods, and applications of Bayesian statistics. Bayesian statistics offers a powerful framework for data analysis and inference, allowing for the incorporation of prior knowledge and uncertainty in a coherent and systematic manner.

Throughout this course, we will cover key concepts such as Bayes' theorem, prior and posterior distributions, likelihood functions, and the fundamental differences between Bayesian and frequentist approaches. You will learn to formulate and estimate statistical models, update beliefs using new data, and make informed decisions based on the posterior probabilities generated through Bayesian inference. By the end of this course, you will possess the necessary skills to perform Bayesian data analysis, interpret results, and apply Bayesian methods in various contexts.

• Postgraduate students and staff
• Further details regarding eligibility criteria are available here

Some familiarity with using Stata is recommended. You may wish to take the module Introduction to Stata' before this module.

The module consists of two sessions. Please begin by watching the pre-recorded lecture videos (session 1 and session 2) before attending the first live Zoom event.

Click the "Booking" panel on the left-hand sidebar (on a phone, this will be via a link called Booking/Availability near the top of the page).

Moodle is the 'Virtual Learning Environment' (VLE) that the SSRMP uses to deliver online courses.

SSRMP lecturers use Moodle to make teaching resources available before, during, and/or after classes, and to make announcements and answer questions.

For this reason, it is vital that all SSRMP students enrol onto and explore their course Moodle pages once booking their SSRMP modules via the UTBS, and that they do so before their module begins. Moodle pages for modules should go live around a week before the module commences, but some may be made visible to students, earlier.

For more information, and links to specific Moodle module pages, please visit our website