The University of Southampton

MATH2012 Stochastic Processes

Module Overview

The module will introduce the basic ideas in modelling, solving and simulating stochastic processes.

Aims and Objectives

Module Aims

To introduce the basic ideas in modelling, solving and simulating stochastic processes. Stochastic processes are systems which change in accordance with probabilistic laws. Examples of such processes are the behaviour of the size of a population, queues, random walks, breakdown of machines. These are all situations which evolve with time according to certain probabilities. However, some are concerned with discrete time, such as the position of a random walk after each step, others with continuous time, like the length of a queue at a supermarket checkout at any time instant. All these are modelled and solved in the module. The mathematical tools used are chiefly difference equations, differential equations, matrices and generating functions. These will be briefly revised at appropriate places in the module.

Learning Outcomes


Markov Chain Definition and basic properties Classification of states and decomposition of state space The long term probability distribution of a Markov chain Modelling using Markov chains Time-homogeneous Markov jump process Poisson process and its basic properties Birth and death processes Kolmogorov differential equations Structure of a Markov jump process Time-inhomogeneous Markov jump process Definition and basics A survival model A sickness and death model A marriage model Sickness and death with duration dependence Basic principles of stochastic modelling Classification of stochastic modelling Postulating, estimating and validating a model Simulation of a stochastic model and its applications Brownian motion: Definition and basic properties. Stochastic differential equations, the Ito integral and Ito formula. Diffusion and mean testing processes. Solution of the stochastic differential equation for the geometric Brownian motion and Ohrnstein-Uhlenbeck process

Learning and Teaching

Teaching and learning methods

Lectures, problem classes, coursework, surgeries and private study

Independent Study96
Total study time150

Resources & Reading list

KULKARNI V G (1999). Modelling, analysis, design and control of stochastic systems. 

HICKMAN J C (1997). Introduction to actuarial modelling. North American Actuarial Journal. ,1 , pp. pg.1-5.

GRIMMETT G and STIRZAKER D (2001). Probability and random processes. 

BRZEZNIAK Z and ZASTAWNIAK T (1998). Basic Stochastic Processes: a course through exercises. 

GRIMMETT G (1992). Probability and random processes: problems and solutions. 

KARLIN S and TAYLOR A (1975). A first course in stochastic process. 



MethodPercentage contribution
Coursework 20%
Written exam 80%


MethodPercentage contribution
Exam 100%

Linked modules

Pre-requisites: MATH2011 OR ECON2006

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