Unit name | Bayesian Modelling |
---|---|
Unit code | MATH30015 |
Credit points | 20 |
Level of study | H/6 |
Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
Unit director | Dr. Gerber |
Open unit status | Not open |
Pre-requisites | |
Co-requisites |
None |
School/department | School of Mathematics |
Faculty | Faculty of Science |
Unit Aims
The aim of the unit is to provide a thorough introduction to the Bayesian approach to statistical analysis and modelling as well as an introduction to the computational tools that make the use of Bayesian methods possible in practice.
Unit Description
The Bayesian approach to statistics relies on the idea that probabilities can be used to express our uncertainty about the quantity of interest and that the prior knowledge of the statistician can be updated using conditional probabilities as observations become available. Bayesian statistics has grown rapidly in popularity over the past 20 years or so largely as a result of computational advances which have made the approach far more applicable. In this unit we will first discuss in detail the Bayesian approach to statistical analysis. Topics discussed will include the construction of prior and posterior distributions, Bayesian decision theory, Bayesian asymptotics and model choice. We will then provide a brief introduction to Markov chain Monte Carlo methods which make Bayesian analysis possible in practice. The last part of unit is devoted to the Bayesian approach to statistical modelling, with emphasis on hierarchical models.
Relation to Other Units
The Theory of Markov chain Monte Carlo methods is covered in more detail in Monte Carlo Methods.
After taking this unit, students will:
The unit will be taught through a combination of
80% Timed, open-book examination 20% Coursework: computing assignments
Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.
If you fail this unit and are required to resit, reassessment is by a written examination in the August/September Resit and Supplementary exam period.
Recommended
Further