Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Construct appropriate parametric statistical models for frequently encountered types of data.
- Derive the asymptotic behaviour of likelihood inference, including the asymptotic distribution of the maximum likelihood estimator and the log-likelihood ratio test statistic.
- Conduct likelihood inference for parametric statistical models, including estimating parameters, constructing large-sample confidence intervals and conducting hypothesis tests.
- Conduct Bayesian inference for parametric statistical models, including choosing a prior distribution, computing the posterior distribution in cases with conjugate and non-conjugate priors, and making predictions and decisions based on the posterior distribution.
Syllabus
Learning and Teaching
Teaching and learning methods
| Type | Hours |
|---|---|
| Teaching | 36 |
| Independent Study | 114 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Wasserman, L (2003). All of Statistics: A Concise Course in Statistical Inference .. Springer..
Sujit K. Sahu (2022). Bayesian modeling of spatio-temporal data. Boca Raton: CRC Press.
Wood, S (2015). Core Statistics. Cambridge University Press.
Gelman, A, Carlin JB, Stern HS, Dunson DB, Vehtari, A and Rubin, DB (2014). Bayesian Data Analysis. CRC Press.
Box, GEP and Tiao, GC (1992). Bayesian Inference in Statistical Analysis. Wiley.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 50% |
| Exam | 50% |
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% |