## Random Models

 Unit code: MATH20712 Credit Rating: 10 Unit level: Level 2 Teaching period(s): Semester 2 Offered by School of Mathematics Available as a free choice unit?: N

#### Requisites

Prerequisite

MATH20712 pre-requisites

#### Aims

The course unit unit aims to enable students to develop some understanding of the way that stochastic processes evolve in time, to become familiar with some simple techniques which help in their study, and to experience some real life applications of stochastic processes.

#### Overview

The course introduces some simple stochastic processes, that is phenomena which evolve in time in a non-deterministic way. It applies the techniques developed in Probability and Statistics 1 and 2 together with the use of generating functions (or power series) to tackle problems such as the gambler's ruin problem, or calculating the probability of the extinction of certain populations.

#### Learning outcomes

On completion of this unit successful students will be able have a good grasp of basic concepts, techniques and results associated primarily with the elementary theory of simple random walks, branching processes, and renewal processes.

#### Assessment methods

• Other - 20%
• Written exam - 80%

#### Assessment Further Information

• Coursework; Weighting within unit 20%
• 2 hours end of semester examination; Weighting within unit 80%

#### Syllabus

1.Review of conditional probability, probability distributions, random variables, means and variances. [2 lectures]

2.Independent random variables. Sums of independent identically distributed random variables. [1]

3.Probability generating functions and their application to sums of independent random variables and random sums. [2]

4.Random walks. Recurrence and transience. Gambler's ruin problem. [7]

5.Branching processes. The size of the nth generation and its probability generating function. The probability of extinction. [6]

6.Renewal processes. The counting processes and occurrence time processes. Renewal equations and real life applications including traffic flow. [6]

â' G.R. Grimmett and D.R. Stirzaker, Probability and Random Processes, Oxford University Press, 2000.

â' S. Karlin and H.M. Taylor, A First Course in Stochastic Processes, Academic Press, 1975.

#### Feedback methods

Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding.  Coursework or in-class tests (where applicable) also provide an opportunity for students to receive feedback.  Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.

#### Study hours

• Lectures - 22 hours
• Tutorials - 11 hours
• Independent study hours - 67 hours

#### Teaching staff

Xiong Jin - Unit coordinator