2010/11 Undergraduate Module Catalogue
MATH2750 Introduction to Markov Processes
10 creditsClass Size: 120
Module manager: Prof A. Veretennikov
Taught: Semester 2 View Timetable
Year running 2010/11
|MATH1715||Introduction to Probability|
This module is approved as an Elective
Module summaryA stochastic process refers to any quantity which changes randomly in time. The number of people in a queue, the capacity of a reservoir, the size of a population, are all examples from the real world. The linking model for all these examples is the simple random walk. The gambler's ruin problem is an example of a simple random walk with two absorbing barriers. Replacing these absorbing barriers with reflecting barriers provides a model for reservoir capacity. With appropriate modifications the random walk can be extended to model stochastic processes which change over continuous time, not just at regularly spaced time points. As a birth-death process this can be used to model population growth, the spread of diseases like AIDS, traffic flow, the queuing of students at a coffee bar, and so on.
ObjectivesTo provide a simple introduction to stochastic processes.
On completion of this module, students should be able to:
(a) have an understanding of, and ability to solve, elementary problems of first passage time distributions
(b) understand about barriers in a random walk
(c) solve equilibrium distribution problems
(d) know the difference between an equilibrium distribution and a stationary distribution
(e) have a knowledge of Markov chains and elementary theory thereof
(f) learn about continuous time Markov process models
(g) have knowledge about the Poisson process
(h) extend the Poisson process model to other simple examples, and solve associated problems
(i) understand the role of forward and backward equations
(j) understand the use of simulation in modelling.
1. Random walks: transition probabilities, first passage time, recurrence, absorbing and reflecting barriers, gambler's ruin problem.
2. Branching chain, probability of ultimate extinction.
3. General theory of Markov chains: transition matrix, Chapman-Kolmogorov equations, classification of states, irreducible Markov chains, stationary distribution, convergence to equilibrium.
4. Poisson process and its properties. Birth-and-death processes, queues.
5. Markov processes in continuous time with discrete state space: transition rates, forward and backward equations, stationary distribution.
6. Simulation of stochastic processes.
|Delivery type||Number||Length hours||Student hours|
|Private study hours||66.00|
|Total Contact hours||34.00|
|Total hours (100hr per 10 credits)||100.00|
Methods of assessment
|Assessment type||Notes||% of formal assessment|
|Total percentage (Assessment Coursework)||20.00|
|Exam type||Exam duration||% of formal assessment|
|Unseen exam (MCQ, essays, etc.)||2 hr||80.00|
|Total percentage (Assessment Exams)||80.00|
Reading listThe reading list is available from the Library website
Last updated: 27/05/2011
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