AIN3100 - Stochastic Processes with Applications

Year of Study:3 - 4
Credit Units: 3
Duration: 45hours
Prerequisites: AMS1302 Probability and Statistical Theory; or with the Module Coordinator’s permission and upon endorsement of the relevant Head
Module Description
This module aims to introduce fundamental discrete time and continuous time stochastic processes for business applications. Topics include review of conditional expectation, discrete time finite state Markov chain, Poisson process, review of normal distribution, Brownian motion, geometric Brownian motion, and applications of these stochastic processes in business.
Learning Outcomes
Upon completion of this module, students should be able to:
  1. apply the concept of conditional distribution and conditional expectation to solve various mathematical problems;

  2. recognise the types of states and the mathematical relationship between states in Markov chain;

  3. analyse Poisson process for modelling counting process and jump process;

  4. analyse the properties of Brownian motion and its variations;

  5. apply concepts involved in stochastic modelling and various types of stochastic processes.