Distance MSc in Operational Research - Stochastic Models

Credit Value: 6
Tutors: Lesley Walls and John Quigley

Class description/introduction

Decision makers are continually confronted with situations where their knowledge is incomplete and as such must endeavour to understand, measure and consequently better manage uncertainty under such complicated situations. Probabilistic modelling is fundamental to appreciating uncertainty and risk. This class provides much of the foundations that underpin the tools and techniques that are taught on other classes of the MSc such as statistics, forecasting, risk and simulation.

Class aims

The aim of the course is to introduce probabilistic modelling, in particular the basic theory and a variety of practical examples to illustrate contribution to decision-making in practice.

Learning outcomes

Subject specific knowledge and skills

• To demonstrate probabilistic reasoning
• To understand the key results for discrete and continuous Markov chains, queueing models, Brownian motion and its variants and Poisson point processes
• To be able to apply stochastic models to a variety of operations problems

Cognitive abilities and non-subject specific skills

• To read mathematical texts to broaden and deepen understanding of probability and its applications
• To write about models and analysis in a coherent manner
• To use appropriate software to implement modelling

Reading List

The following may be of use but are not required:

• Higgins JJ and Keller-McNulty S (1995) Concepts in Probability and Stochastic Modelling, Duxbury Press
• Ross SM (1994) Introduction to Probability Models, 5 ed, Academic Press
• Ross SM (1996) Stochastic Processes, 2 ed, Wiley.
• Tijms HC (1995) Stochastic Models: An Algorithmic Approach, Wiley

Assessment

There is a formal 2-hour exam in the spring diet.

Learning Outcomes

• To demonstrate probabilistic reasoning
• To understand the key results for discrete and continuous Markov chains, queueing models, Brownian motion and its variants and stochastic point processes
• To be able to apply stochastic models to a variety of operations problems
• To read mathematical texts to broaden and deepen understanding of probability and its applications
• To write about models and analysis in a coherent manner
• To use appropriate software to implement modelling

management science DEPARTMENT OF MANAGEMENT SCIENCE
UNIVERSITY OF STRATHCLYDE Graham Hills BUILDING 40 GEORGE STREET G1 1QE
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contact-mansci@strath.ac.uk