20 Credits ACADEMIC YEAR



Aims/Description: This unit is largely concerned with practical statistical inference. Modern computational tools for the implementation of the frequentist and likelihood-based approaches to inference are explored, with strong emphasis placed in the use of simulation and Monte Carlo methods. Statistical theory is also developed with an introduction to the Bayesian approach to inference and decison making. Computational methods for practical Bayesian inference will also be covered.

Staff Contact: OAKLEY JEREMY E
Teaching Methods: Lectures, Independent Study,
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 Individual modules are occasionally updated or withdrawn.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