• Inactive for Year: 2026/27

• Module Leader(s): Professor Murray Pollock
• Lecturer: Dr Jere Koskela

• 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 stochastic simulation and probabilistic approaches for modelling and analysing complex real-world systems and datasets under uncertainty. You will study methods for generating and analysing random samples, including Monte Carlo and sequential Monte Carlo techniques, alongside probabilistic approaches for inference, estimation, and modelling within complex and partially observed environments. The emphasis is on understanding how uncertainty propagates through computational models, how complex behaviour can be explored through simulation, and how stochastic methods are applied to challenging real-world datasets, including temporal and signal-type data.

Outline Of Syllabus

Topics covered by this module include:

Simulation of random variables and probabilistic systems
Sampling methods for univariate and multivariate probability distributions
Rejection sampling and importance sampling techniques
Monte Carlo integration and stochastic simulation methods
Markov chain Monte Carlo methods
Sequential Monte Carlo methods and particle filtering
Probabilistic inference and uncertainty propagation
Temporal and signal-type data modelling
Autoregressive and state-space modelling approaches
Kalman filtering and recursive state estimation
Applications of stochastic simulation and probabilistic modelling to complex real-world systems and datasets

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 simulation, probabilistic modelling, and Monte Carlo approaches within controlled conditions. The examination evaluates students’ ability to reason about sampling methods, simulation-based inference, uncertainty propagation, stochastic processes, and probabilistic modelling approaches within both theoretical and applied contexts. The examination assesses all module learning outcomes. (MLO1, MLO2, MLO3, MLO4)



The report provides students with the opportunity to apply stochastic simulation and probabilistic modelling approaches within an applied analytical setting. Students will construct, evaluate, and interpret simulation-based models and communicate findings through a structured technical report. The assessment particularly supports the development and evaluation of practical modelling, simulation, and analytical skills associated with generating insights and supporting decision making under uncertainty. (MLO4)



The formative report provides opportunities for students to practise simulation-based analysis, modelling, and technical communication skills while receiving feedback in advance of the summative assessments.



The assessment structure supports the development of both conceptual understanding and applied computational skills, enabling students to evaluate, construct, and interpret stochastic and simulation-based models within complex real-world 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

• DSC3006's Reading List

Timetable

• Timetable Website: www.ncl.ac.uk/timetable/
• DSC3006's Timetable