STAT40810 Stochastic Models (online)

Academic Year 2023/2024

This module covers the principles behind, and key characteristics, of stochastic models in real-world applications. Topics include: introduction to stochastic modelling with applications; non-parametric regression; Survival analysis, Markov chains, continuous time Markov processes, Poisson processes; mixture models.

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Curricular information is subject to change

Learning Outcomes:

The student will be able to construct a statistical model, fit the model to data and judge when a model is satisfactory. The student will be familiar with a number of statistical model types. The student will be able to apply statistical models to real world applications and assess the adequacy of the applied models.

Indicative Module Content:

Introduction to modeling
Model selection
Non-Parametric regression and smoothing
Survival models
Markov chains
Poisson processes
Finite mixture models

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning


Online Learning




Approaches to Teaching and Learning:
The course will be delivered through an online format. The lectures are recorded as videos. The notes included worked examples and computer code. The course involves mini-projects in statistical modeling. 
Requirements, Exclusions and Recommendations
Learning Recommendations:

Knowledge of probability theory and basic statistics. Good knowledge in calculus (integrals, differentials) and linear algebra (vectors, matrices, eigenvalues).

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Multiple Choice Questionnaire: Online quiz in brightspace. Week 12 n/a Standard conversion grade scale 40% No


Continuous Assessment: Online assessments involving statistical modeling mini-projects Throughout the Trimester n/a Other No


Carry forward of passed components
Resit In Terminal Exam
Autumn Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

After the deadline for the assessments, an outline assessment solution will be provided. Feedback will be given to students along with their marks for their assessments.

Name Role
Dr Niamh Cahill Lecturer / Co-Lecturer
Professor Brendan Murphy Lecturer / Co-Lecturer
Mr Brian Buckley Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.

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