30 Credits ACADEMIC YEAR

Cannot be taken with: MAS364


Aims/Description: This module introduces the Bayesian approach to statistical inference. The Bayesian method is fundamentally different in philosophy from conventional frequentist/classical inference, and has been the subject of some controversy in the past, but is now widely used. The module also presents various computational methods for implementing both Bayesian and frequentist inference, in situations where obtaining results `analytically' would be impossible. The methods will be implemented using the programming languages R and Stan, and some programming is taught alongside the theory lectures.

Restrictions on availability: For students on MASU02, MASU39, MASU10, MASU12, MASU13, MASU14, MASU15, MASU41, MASU43: pre-requisite module is MAS223 Statistical Modelling

Staff Contact: JUAREZ MIGUEL A
Teaching Methods: Lectures, Independent Study, Computer practicals
Assessment: Formal Exam, Project/ portfolio

Information on the department responsible for this unit (Mathematics and Statistics):

Departmental Home Page
Teaching timetable

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NOTE
The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.

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Teaching methods and assessment displayed on this page are indicative for 2021-22. Students will be informed by the academic department of any changes made necessary by the ongoing pandemic.

Western Bank, Sheffield, S10 2TN, UK