MAS3902 : Bayesian Inference
- Offered for Year: 2017/18
- Module Leader(s): Dr Wentao Li
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
|Semester 1 Credit Value:||10|
To gain an understanding of the principles and the practical applications of Bayesian Statistics to more complex models relevant to practical data analysis. To improve data-analytic and report-writing skills through group project work.
The course builds on the foundations of Bayesian inference laid in MAS2903. We consider extensions to models with more than a single parameter and how these can be used to analyse data. We also provide an introduction to modern computational tools for the analysis of more complex models for real data.
Outline Of Syllabus
Review of Bayesian inference for single parameter models. Inference for multi-parameter models using conjugate prior distributions: mean and variance of a normal random sample. Asymptotic posterior distribution for multi-parameter models. Introduction to Markov chain Monte Carlo methods: Gibbs sampling, Metropolis-Hastings sampling, mixing and convergence. Application to random sample models using conjugate and non-conjugate prior distributions. Computation using R.
|Scheduled Learning And Teaching Activities||Lecture||3||1:00||3:00||Problem classes|
|Scheduled Learning And Teaching Activities||Lecture||2||1:00||2:00||Revision lectures|
|Scheduled Learning And Teaching Activities||Lecture||25||1:00||25:00||Formal lectures|
|Guided Independent Study||Assessment preparation and completion||1||13:00||13:00||Revision for unseen exam|
|Guided Independent Study||Assessment preparation and completion||1||2:00||2:00||Unseen exam|
|Guided Independent Study||Independent study||1||22:00||22:00||Studying, practising and gaining understanding of course material|
|Guided Independent Study||Independent study||3||2:30||7:30||Review of problem-solving exercises and group project|
|Guided Independent Study||Independent study||1||12:00||12:00||Preparation for group project|
|Guided Independent Study||Independent study||3||4:30||13:30||Preparation for problem-solving exercises|
Teaching Rationale And Relationship
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem Classes are used to help develop the students’ abilities at applying the theory to solving problems. Tutorials are used to identify and resolve specific queries raised by students and to allow students to receive individual feedback on marked work. In addition, office hours (two per week) will provide an opportunity for more direct contact between individual students and the lecturer.
The format of resits will be determined by the Board of Examiners
|Prob solv exercises||1||M||5||Problem solving exercises|
|Prob solv exercises||1||M||10||Group project|
Assessment Rationale And Relationship
A substantial formal unseen examination is appropriate for the assessment of the material in this module. The problem solving exercises are expected to consist of three exercises of equal weight: the exact nature of assessment will be explained at the start of the module. The exercises and the group project allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; these are thus formative as well as summative assessments.
- Reading List Website : rlo.ncl.ac.uk