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Module Descriptor School of Computer Science and Statistics

Module CodeST3458
Module Short TitleClassical inference
Semester Taught2
Contact Hours3 hours per week, some of which will be tutorials
Module PersonnelJason Wyse
Learning OutcomesAfter taking this course the student will have a clear understanding of the mechanisms underlying many hypothesis tests and confidence intervals. The course will include a full treatment of estimation and properties of estimators, as well as a light introduction to statistical asymptotics.
Learning AimsUnderstand the theory of distributions necessary to build tests/confidence intervals. Learn how to construct confidence intervals based on pivots and large sample approximations. Derive maximum likelihood and method of moments estimators for well known distributions. Learn how to construct hypothesis tests for parameters. Derive properties of estimators, including bias and mean squared error. Understand asymptotic properties of maximum likelihood estimators.
Module ContentAs in learning aims.
Recommended Reading ListThere are many good introductory texts for mathematical statistics. 'Statistical Inference' by Berger and Casella contains much of the material relevant to this course.
Module Prerequisites
Assessment Details90% final exam and 10% assignments.
Module Website
Academic Year of Data2015/16