MAS8405 : Bayesian Data Analysis (Inactive)
MAS8405 : Bayesian Data Analysis (Inactive)
- Inactive for Year: 2024/25
- Module Leader(s): Professor Chris Oates
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
| Code | Title |
|---|---|
| MAS8404 | Statistical Learning for Data Science |
Pre Requisite Comment
N/A
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
Bayesian analysis provides a principled approach for the synthesis of information from different sources. These principles have been adopted across a wide variety of areas within science, industry, machine learning, and AI. This has been made possible by the development of powerful computational algorithms for Bayesian analysis. This module starts with an introduction to the principles of Bayesian analysis before moving on to more complex models and computational algorithms relevant to practical data analysis.
Specifically, the module aims to equip students with the following knowledge and skills:
To gain an understanding of the principles and the practical applications of the Bayesian approach to data analysis.
To gain knowledge of modern computational methods for Bayesian analysis and their application to real-world problems.
Outline Of Syllabus
Principles of Bayesian inference
Conjugate priors
Computational methods, such as Markov chain Monte Carlo (MCMC)
Applications, such as: linear models, generalized linear models, mixture models, hidden Markov models, dynamic linear models, Gaussian process regression
Learning Outcomes
Intended Knowledge Outcomes
At the end of the module, students will be familiar with the theory and practicalities of modern Bayesian computational methods and will have a knowledge of the application of these ideas in a range of industrial problems.
Intended Skill Outcomes
Students will have increased statistical computing skills and enhanced report writing skills. They will be able to analyse a range of data using Bayesian methods and implement these using appropriate software.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 14:00 | 14:00 | Formative report |
| Structured Guided Learning | Lecture materials | 6 | 2:00 | 12:00 | Present in person lectures, which are also recorded and made available via Recap |
| Scheduled Learning And Teaching Activities | Practical | 6 | 2:00 | 12:00 | Present in person practical |
| Guided Independent Study | Project work | 1 | 32:00 | 32:00 | Main project |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 6 | 2:00 | 12:00 | present in person drop-in |
| Guided Independent Study | Independent study | 6 | 3:00 | 18:00 | Lecture follow-up/background reading |
| Total | 100:00 |
Teaching Rationale And Relationship
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples. Practicals are used both for solution of problems and to give insight into the ideas/methods studied. There are present-in-person practical sessions and present-in-person drop-in sessions each week to ensure rapid feedback on understanding.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 100 | A compulsory report allowing students to demonstrate skills and knowledge gained during the course. |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Practical/lab report | 2 | M | A compulsory report allowing students to develop problem solving techniques & to practise the methods learnt and to assess progress |
Assessment Rationale And Relationship
A compulsory formative practical report allows the students to develop their problem-solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback, before the summative assessments.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MAS8405's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- MAS8405's past Exam Papers
General Notes
N/A
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The information contained within the Module Catalogue relates to the 2024 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
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