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|
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:
|MAS352||Stochastic Processes and Finance|
|MAS452||Stochastic Processes and Finance|
- 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.
- 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.
Introduction to Markov chains
Definition, transition probabilities, examples including random walks and gambler's ruin, Chapman-Kolmogorov, stationary distributions.
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.
|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|