Statistical Inference (L.6) (G5216)
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Statistical Inference (L.6)
Module G5216
Module details for 2025/26.
15 credits
FHEQ Level 6
Module Outline
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 and Sufficiency
b. Point estimators
c. Hypothesis Testing
d. Interval estimators (confidence intervals and their connection with hypothesis tests)
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.
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
Understand and appreciate the difference between the Frequentist and Bayesian Statistics
To be able to identify appropriate test and write programs/code to test theoretical results from Frequentist and Bayesian Statistics.
Develop skills to appreciate the application and implementation of theory in an applied context.
| Type | Timing | Weighting |
|---|---|---|
| Unseen Examination | Semester 2 Assessment | 80.00% |
| Coursework | 20.00% | |
| Coursework components. Weighted as shown below. | ||
| 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% |
| Portfolio | T2 Week 11 | 40.00% |
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 | Practical | 1 hour | 01010101010 |
| Spring Semester | Lecture | 2 hours | 11111111111 |
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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