MAS8953 : Computational Statistics
MAS8953 : Computational Statistics
- Offered for Year: 2024/25
- Module Leader(s): Dr Murray Pollock
- 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 1 Credit Value: | 20 |
ECTS Credits: | 10.0 |
European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Code | Title |
---|---|
MAS3902 | Bayesian Inference |
MAS3917 | Stochastic Processes |
Pre Requisite Comment
N/A
Co-Requisite
Modules you need to take at the same time
Code | Title |
---|---|
MAS8954 | Advanced Topics in Statistics A |
MAS8955 | Advanced Topics in Statistics B |
Co Requisite Comment
N/A
Aims
To develop further an understanding of Bayesian inference, to gain knowledge of modern applications of Bayesian inference and modern Bayesian computational methods and to acquire skill in Bayesian modelling, data analysis and computation.
Module summary
In recent years great advances have been made in the application of Bayesian statistical inference to problems in a wide variety of areas. This has been made possible by the development of computational algorithms which allow posterior distributions to be found in complicated models. This module extends the introductory material on Bayesian inference given in MAS3902 and, in particular, look at more complex models.
Outline Of Syllabus
Review of Bayesian inference, particularly non-conjugate scenarios. Introduction to Monte Carlo methods. Review of Markov chain Monte Carlo (MCMC), including Gibbs sampling and Metropolis-Hastings; assessment of mixing and convergence. Theoretical foundations of MCMC. Some advanced topics in computational statistics covered may include Approximate Bayesian computation and Sequential Monte Carlo methods.
Applications considered may include: linear models, generalized linear models, missing data problems, data augmentation, mixture models and hierarchical models.
Learning Outcomes
Intended Knowledge Outcomes
On completion of the course, students will be familiar with the theory and practicalities of computational methods and will have a knowledge of the application of these ideas in a range of problem types.
Intended Skill Outcomes
Students will have increased statistical computing skills and enhanced report writing skills. They will be able to construct a range of computational algorithms suitable for a variety of models and implement these using appropriate software. They will be able to use this knowledge to make inferences from data in such cases.
Students will develop skills across the cognitive domain (Bloom’s taxonomy, 2001 revised edition): remember, understand, apply, analyse, evaluate and create
Teaching Methods
Teaching Activities
Category | Activity | Number | Length | Student Hours | Comment |
---|---|---|---|---|---|
Scheduled Learning And Teaching Activities | Lecture | 40 | 1:00 | 40:00 | Formal Lectures |
Guided Independent Study | Assessment preparation and completion | 30 | 1:00 | 30:00 | Completion of in course assessments |
Scheduled Learning And Teaching Activities | Lecture | 4 | 1:00 | 4:00 | Revision Lectures |
Scheduled Learning And Teaching Activities | Practical | 10 | 1:00 | 10:00 | Computer Practical |
Guided Independent Study | Independent study | 116 | 1:00 | 116:00 | Preparation time for lectures, background reading, coursework review |
Total | 200:00 |
Teaching Rationale And Relationship
The teaching methods are appropriate to allow students to develop a wide range of skills, from understanding basic concepts and facts to higher-order thinking. 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 and computer practicals are used to help develop the students’ abilities at applying the theory to solving problems.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
Description | Length | Semester | When Set | Percentage | Comment |
---|---|---|---|---|---|
Written Examination | 150 | 1 | A | 80 | N/A |
Other Assessment
Description | Semester | When Set | Percentage | Comment |
---|---|---|---|---|
Prob solv exercises | 1 | M | 10 | Extended Programming Assignment |
Written exercise | 1 | M | 5 | In class test |
Written exercise | 1 | M | 5 | In class test |
Assessment Rationale And Relationship
A substantial formal unseen examination is appropriate for the assessment of the material in this module. The format of the examination will enable students to reliably demonstrate their own knowledge, understanding and application of learning outcomes. The assurance of academic integrity forms a necessary part of the programme accreditation.
Examination problems may require a synthesis of concepts and strategies from different sections, while they may have more than one ways for solution. The examination time allows the students to test different strategies, work out examples and gather evidence for deciding on an effective strategy, while carefully articulating their ideas and explicitly citing the theory they are using.
The coursework assignments and in-class tests 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 assessments have a secondary formative purpose as well as their primary summative purpose.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MAS8953's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- MAS8953's past Exam Papers
General Notes
N/A
Welcome to Newcastle University Module Catalogue
This is where you will be able to find all key information about modules on your programme of study. It will help you make an informed decision on the options available to you within your programme.
You may have some queries about the modules available to you. Your school office will be able to signpost you to someone who will support you with any queries.
Disclaimer
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.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2025/26 entry will be published here in early-April 2025. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.