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Curricular information is subject to change
By the end of the course, students should be able to:
- identify which distribution is appropriate for certain types of data
- understand the purposes of inference and how to do it in practice
- estimate parameters and their associated uncertainty via likelihood methods, and interpret these values in the context of real-world problems.
- incorporate prior information into common statistical problems and obtain posterior probability distributions of parameters of interest.
Background of what is inference, recap on probability theory, random variables, common distributions for data.
Random vectors, independence, and conditional distributions.
Expectation, covariance, correlation.
Properties of random samples and asymptotics.
Frequentist statistical inference (method of moments, MLE, confidence intervals).
Uncertainty of estimates: parametric and non-parametric.
Numerical inferential algorithms.
Introduction to Bayesian inference.
|Student Effort Type||Hours|
|Specified Learning Activities||
|Autonomous Student Learning||
Calculus: familiarity with differentiation and integration. Students are required to have completed an introductory statistics module such as STAT10060 or STAT40720.
|Description||Timing||Component Scale||% of Final Grade|
|Examination: 2 hour end of trimester online written exam||2 hour End of Trimester Exam||Yes||Other||No||
|Continuous Assessment: Tutorial sheets and computer lab exercises||Throughout the Trimester||n/a||Other||No||
|Resit In||Terminal Exam|
|Summer||Yes - 2 Hour|
• Group/class feedback, post-assessment
Not yet recorded.
|Dr John O'Sullivan||Tutor|