• Inactive for Year: 2026/27

• Module Leader(s): Dr Giorgos Vasdekis
• Lecturer: Dr Vianey Palacios Ramirez

• 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.

Aims

This module develops your understanding of probabilistic and stochastic approaches for modelling systems that evolve under uncertainty. You will study stochastic processes, Bayesian methods, and probabilistic models used to represent dynamic and real-world phenomena, alongside approaches for inference, prediction, and analysis within uncertain environments. The emphasis is on developing intuition for probabilistic modelling and applying these methods to the analysis and interpretation of evolving systems and data.

Outline Of Syllabus

Topics covered by this module include:


Bayesian statistical models
Bayesian inference
Random walks, Markov chains
Transition probabilities
Classification of states
Invariant and reversible distributions
Applications for dynamical data

Teaching Methods

Teaching Activities

Teaching Rationale And Relationship

This module includes an encounter with the leading edge of research.

Assessment Methods

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

Exams

Other Assessment

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.

Assessment Rationale And Relationship

The written examination assesses students’ understanding and application of stochastic and probabilistic modelling approaches within controlled conditions. The examination evaluates students’ ability to reason about stochastic processes, Bayesian inference, Markov chains, uncertainty, and model behaviour within both theoretical and applied contexts. The examination assesses all module learning outcomes and supports the evaluation of technical and mathematical competence. (MLO1, MLO2, MLO3, MLO4)


The coursework report provides students with the opportunity to apply stochastic modelling techniques and probabilistic reasoning to structured problem-solving exercises within an applied setting. The assessment supports the development of analytical and interpretative skills associated with evaluating uncertainty, model behaviour, and practical applications of stochastic methods. The coursework particularly supports the assessment of application-focused learning outcomes. (MLO3, MLO4)


The formative exercises provide opportunities for students to practise stochastic modelling and probabilistic analysis techniques and receive feedback on their understanding in advance of the summative assessments.


The assessment structure supports the development of both theoretical understanding and applied analytical skills, enabling students to develop confidence in the use, interpretation, and evaluation of stochastic and probabilistic models within practical contexts.


Alternative assessment:


For students with a Student Support Plan (SSP) who have alternative assessment as a reasonable adjustment and/or students with supported Personal Extenuating Circumstances (PEC), it may be necessary to set an alternative assessment as presented below. Due to the professional requirements of some programmes, alternative assessments may not be available even where students are eligible.


An oral examination comprising a 15-minute prepared topic followed by 15 minutes of questions from the overall module syllabus may be offered as an alternative to the written examination where appropriate.

Reading Lists

• DSC2009's Reading List

Timetable

• Timetable Website: www.ncl.ac.uk/timetable/
• DSC2009's Timetable