Statistical Inference (L.7) (867G1)
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Statistical Inference (L.7)
Module 867G1
Module details for 2025/26.
15 credits
FHEQ Level 7 (Masters)
Module Outline
Module description
Part 0: Revision of Probability a. Random Variables and probability distributions. b. Revision of some well known probability distributions c. Expectation and interpretation of moments. d. Conditional Probability and Bayes’ rule e. Conditional Expectation and properties.
Part 1: Frequentist Statistics a. Likelihood, Sufficiency and Ancillarity. b. Point estimators c. Hypothesis Testing d. Interval estimators (confidence intervals and their connection with hypothesis tests) e. Asymptotic Theory (consistency, asymptotic normality, chi square approximation).
Part 2: Bayesian Statistics a. The Bayesian Paradigm b. Bayesian Models c. Prior Distributions.
Part 3: Model Selection a. Frequentist Model Selection b. Bayesian Model selection and Bayes Factors.
Throughout this module, numerous practical real-world examples will be discussed during practical sessions and analysed using the R programming language.
Library
Textbooks:
1. Casella, G. and Berger, R.L. (2002) Statistical Inference. Second Edition. Thomson Learning.
2. Cox D.R. (2006), Principles of Statistical Inference, Cambridge University Press.
3. Garthwaite, P.H., Joliffe, I.T. and Jones, B. (2002) Statistical Inference. Second Edition. Oxford University Press.
4. Leonard, T. and Hsu, J.S.J. (2001) Bayesian Methods. Cambridge, University Press.
Module learning outcomes
Acquisition of the following knowledge and understanding: Understand the concepts and methods of statistical inference and be able to apply these methods in practical situations and as a part of a decision making process.
Command of the following intellectual and practical skills: Write programs for Bayesian inference and model selection.
Analyse, interpret and critically appraise articles on Statistics.
Develop scientific and technical writing skills for continuing professional development
| Type | Timing | Weighting |
|---|---|---|
| Unseen Examination | Semester 2 Assessment | 80.00% |
| Coursework | 20.00% | |
| Coursework components. Weighted as shown below. | ||
| Portfolio | T2 Week 11 | 40.00% |
| Problem Set | T2 Week 4 | 15.00% |
| Problem Set | T2 Week 10 | 15.00% |
| Software Exercise | T2 Week 6 | 15.00% |
| Software Exercise | T2 Week 11 | 15.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Lecture | 1 hour | 10101010101 |
| Spring Semester | Lecture | 2 hours | 11111111111 |
| Spring Semester | Practical | 1 hour | 01010101010 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr Chris Hadjichrysanthou
Assess convenor, Convenor
/profiles/211204
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