Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Understand concept of a hypothesis test and confidence intervals and how to construct these with good properties
- Derive generating functions and use them to determine distributions and moments
- Understand properties of maximum likelihood estimators
- Understand concepts of Bayesian inference
- Find distributions of functions of random variables, including distributions of maximum and minimum observations, and use these results to derive methods to simulate observations from standard distributions
- Evaluate different estimators using their theoretical properties;
Syllabus
Mgfs and Cgfs
Transformations of random variables (univariate, bivariate, sums and extremes, include derivation of t, F and Beta)
Point estimation (unbiased, MSE, consistency, sufficiency, MVUE, Rao-Blackwell)
Revision of ML
Asymptotics of ML
Hypothesis testing (Uniformly most powerful test & Neyman-Pearson lemma)
Confidence intervals
Bayesian inference
Learning and Teaching
Teaching and learning methods
Lectures, problem classes
Type | Hours |
---|---|
Teaching | 48 |
Independent Study | 102 |
Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Exam | 70% |
Coursework | 30% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Exam | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Exam | 100% |