Aims and Fit of Module

This module aims to cement a solid foundation in theoretical teaching of stochastic processes and their applications in statistics. It provides a user-friendly introduction to stochastic process and appropriate training in random simulation. This is a required module for students in statistics. The designer/instructor of this module believes it is helpful for students to understand basic modeling skills through study of the fundamental knowledge of stochastic processes.

Learning outcomes

A	Demonstrate an understanding of Markov chain and its long-term behavior.
B	Demonstrate an understanding of the fundamentals of the algorithms of Markov Chain and Monte Carlo
C	Demonstrate an understanding of Poisson process and continuous-time Markov Chain
D	Demonstrate an understanding of the concept of Brownian motion and Gaussian process
E	Use Ito formula

Method of teaching and learning

This module will be delivered by a combination of formal lectures and tutorials.