Please see the full Module Specifications for background information relating to all of the APTS modules, including how to interpret the information below.
Aim: The main aim of this module is to introduce important general aspects of statistical modelling, including Bayesian modelling. A broad range of specific, commonly-used types of model will be encountered.
Learning outcomes: After taking this module, students should --- for topics listed below which are included in the module --- understand the issues (why this is important), the terminology, the statistical principles associated with this aspect of modelling, and sufficient theory to deal with simple examples; and they will have gained some practical hands-on experience in more complex examples.
Prerequisites: Preparation for this module should (re-)establish familiarity with linear and generalized linear models, and with likelihood and Bayesian inference. Students who are familiar with (for example) chapters 4, 8, 10 and 11 of Davison (2003) ``Statistical Models'' will be very well prepared (and will already know something of the areas to be covered in the module).
- Principles and practice of model selection;
- Random-effects/hierarchical/mixed models;
- The role of conditional independence in modelling;
- Non-linear models.
Assessment: Exercises set by the module leader, which will include some practical data analysis and statistical modelling.