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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

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


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.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion301:0030:00Completion of in course assessments
Scheduled Learning And Teaching ActivitiesLecture41:004:00Revision Lectures
Scheduled Learning And Teaching ActivitiesLecture401:0040:00Formal Lectures
Scheduled Learning And Teaching ActivitiesPractical101:0010:00Computer Practical
Guided Independent StudyIndependent study1161:00116:00Preparation time for lectures, background reading, coursework review
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.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Description Length Semester When Set Percentage Comment
Written Examination1501A80N/A
Other Assessment
Description Semester When Set Percentage Comment
Prob solv exercises1M10Extended Programming Assignment
Written exercise1M5In class test
Written exercise1M5In 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.

Reading Lists