10 Credits AUTUMN

Cannot be taken with: MAS364
Pre-requisites: MAS223

70 credits of Level 3 statistics modules or equivalent


Aims/Description: This unit develops 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. It is, however, becoming increasingly popular in many fields of applied statistics. This course will cover both the foundations of Bayesian statistics, including subjective probability, utility and decision theory, and modern computational tools for practical inference problems, specifically Markov chain Monte Carlo methods and Gibbs sampling. Applied Bayesian methods will be demonstrated in a series of case studies using the software package WinBUGS.

Staff Contact: Professor Paul Blackwell
Teaching Methods: Lectures, Problem solving, Independent Study,
Assessment: Formal Exam, Course work

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

Departmental Home Page
Teaching timetable

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NOTE
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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