BAYESIAN INFERENCE FOR DATA SCIENCES - 2023/4
Module code: MAT3052
Module Overview
The module looks at the branch of statistics called Bayesian Statistics. It relies on subjective probability and looks at why this is extremely useful for modelling realistic problems. The module covers an introduction to Bayesian statistics, incorporating prior to posterior analysis for a wide range of statistical models. This shows the students an alternative approach to the Classical statistics that they have studied so far and looks at various statistical techniques that they have studied before and gives them a Bayesian approach.
Module provider
Mathematics & Physics
Module Leader
SANTITISSADEEKORN Naratip (Maths & Phys)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 6
Module cap (Maximum number of students): 150
Overall student workload
Independent Learning Hours: 106
Lecture Hours: 5
Seminar Hours: 11
Laboratory Hours: 6
Guided Learning: 11
Captured Content: 11
Module Availability
Semester 1
Prerequisites / Co-requisites
MAT2013 Mathematical Statistics
Module content
Indicative content includes:
- review of distribution theory
- subjective probability and prior distributions
- noninformative and conjugate
- prior to posterior analysis
- predictive inference
- Bayesian estimation and hypothesis testing
- application to linear models
- approximate methods to estimation
- elements of decision theory and comparative inference
- Gibbs sampling
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Coursework | Assessed Coursework | 25 |
| Examination | Final examination (2 hr) | 75 |
Alternative Assessment
N/A
Assessment Strategy
The assessment strategy is designed to provide students with the opportunity to demonstrate:
- Understanding of and ability to interpret and manipulate mathematical statements.
- Subject knowledge through the recall of key definitions, theorems and their proofs.
- Analytical ability through the solution of unseen problems in the test and exam.
- Ability to implement computational Bayesian method and use visualisation tools to present the result via statistical software such as Python
Thus, the summative assessment for this module consists of:
- One two-hour examination; worth 75% module mark.
- One assessed coursework; worth 25% module mark.
Formative assessment and feedback: Students receive written feedback via marked coursework assignment
Module aims
- Introduce the rationale for, the main techniques of, and general issues in Bayesian statistics
- Apply techniques to standard statistical models such as normal linear models
- Apply Bayesian approaches to estimation and testing
- Introduce Bayesian prediction
- Consider the role of decision theory
- Computational Bayesian technique using Gibbs sampling
Learning outcomes
| Attributes Developed | ||
| 001 | Analyse the differences between the Bayesian paradigm and frequentist statistical methods | CK |
| 002 | Calculate the posterior and predictive distribution and related quantities | CK |
| 003 | Define hierarchical models and state and prove related theorems | CK |
| 004 | Demonstrate how models can be written in hierarchical form and calculate posterior quantities | CKPT |
| 005 | Explain the arguments for and against the Bayesian paradigm | CK |
| 006 | Demonstrate coding skills to apply Bayesian inference to analyse real-world data and predictive analysis | PT |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to provide:
- A detailed introduction to the theory behind, methodology and approaches used in Bayesian statistics
- Experience (through demonstration) of the methods used to interpret, understand and solve problems in analysis
The learning and teaching methods include:
- face-to-face seminars and lectures, supported by face-to-face computer lab sessions designed to develop programming skills to apply Bayesian inference to analyse real-world data.
- online recorded material designed to complement the face-to-face sessions.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MAT3052
Other information
N/A
Programmes this module appears in
| Programme | Semester | Classification | Qualifying conditions |
|---|---|---|---|
| Economics and Mathematics BSc (Hons) | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Mathematics with Statistics MMath | 1 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |
| Mathematics with Statistics BSc (Hons) | 1 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |
| Mathematics BSc (Hons) | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Mathematics with Music BSc (Hons) | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Financial Mathematics BSc (Hons) | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Financial Data Science MSc | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Mathematics MMath | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.