MAS275 Probability Modelling

Note: This is an old module occurrence.

You may wish to visit the module list for information on current teaching.

Semester 2, 2017/18 10 Credits
Lecturer: Dr Timothy Heaton Home page Timetable Reading List
Aims Outcomes Assessment Full Syllabus

The course introduces a number of general models for processes where the state of a system is fluctuating randomly over time. Examples might be the length of a queue, the size of a reproducing population, or the quantity of water in a reservoir. The aim is to familiarize students with an important area of probability modelling.

Prerequisites: MAS113 (Introduction to Probability and Statistics); MAS211 (Advanced Calculus and Linear Algebra)

The following modules have this module as a prerequisite:

MAS352Stochastic Processes and Finance
MAS371Applied Probability
MAS452Stochastic Processes and Finance

Outline syllabus

  • Introduction to Markov chains
  • Discrete time renewal theory
  • Limiting behaviour of Markov chains
  • Applications of Markov chains
  • Hitting times and probabilities
  • Poisson processes


  • To introduce and study a number of general models for processes where the state of a system is fluctuating over a period of time according to some random mechanism.
  • To illustrate the above models by example and by simulation.
  • To familiarise students with an important area of probability modelling.

Learning outcomes

  • model a range of situations by writing down transition matrices for suitable Markov chains.
  • calculate and interpret equilibrium probabilities and distributions of Markov chains.
  • calculate and interpret absorption probabilities and expected times to absorption in Markov chains.
  • understand the special properties of the simple Poisson process, perform calculations with them and interpret the results.
  • understand the spatial and inhomogeneous extensions of the Poisson process, and apply them as models of real phenomena.

22 lectures, 5 tutorials


One formal 2 hour closed book examination.

Full syllabus

Introduction to Markov chains
Definition, transition probabilities, examples including random walks and gambler's ruin, Chapman-Kolmogorov, stationary distributions.

Discrete time renewal processes
Definition, generating functions, delayed renewal processes.
Limiting behaviour of Markov chains
Classification of states, link to renewal theory, limit theorem, periodic and non-irreducible chains, the renewal theorem.
Applications of Markov chains
Google PageRank.
Hitting times and probabilities
Hitting probabilities, expected hitting times.
Poisson processes
Poisson process: superposition, thinning, conditioning on number of events in an interval. Inhomogeneous and spatial generalisations.

Reading list

Type Author(s) Title Library Blackwells Amazon
C E. Parzen Stochastic Processes 519.23 (P) Blackwells Amazon
C G.R. Grimmett, D.R.Stirzaker Probability and Random Processes 519.2 (G) Blackwells Amazon
C S.M. Ross Introduction to Probability Models 519.2 (R) Blackwells Amazon
C W. Feller An Introduction to Probability Theory and its Applications

(A = essential, B = recommended, C = background.)

Most books on reading lists should also be available from the Blackwells shop at Jessop West.


Tue 13:00 - 13:50 lecture   Hicks Lecture Theatre 1
Wed 12:00 - 12:50 tutorial (group 3b) (even weeks) Hicks Lecture Theatre 10
Thu 11:00 - 11:50 tutorial (group 1b) (even weeks) Arts Tower Lecture Theatre 5
Thu 12:00 - 12:50 tutorial (group 2b) (even weeks) Hicks Lecture Theatre 2
Thu 12:00 - 12:50 tutorial (group 4b) (even weeks) Hicks Lecture Theatre 4
Fri 11:00 - 11:50 lecture   Students Union Auditorium