Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- 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.
- Construct appropriate parametric statistical models for frequently encountered types of data.
- 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
Wood, S (2015). Core Statistics. Cambridge University Press.
Sujit K. Sahu (2022). Bayesian modeling of spatio-temporal data. Boca Raton: CRC 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.
Wasserman, L (2003). All of Statistics: A Concise Course in Statistical Inference .. Springer..
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% |