Year of Study:	3 - 4
Credit Units:	3
Duration:	45hours
Prerequisites:	AMS1302 Probability and Statistical Theory or with the Instructor’s permission and upon endorsement of the relevant Chairperson or Programme Director.

This module aims to develop students’ knowledge and understanding of modern statistical concepts and methods. Major topics include advanced theories and methods of estimation (e.g. minimum variance unbiased estimators, methods of moments and maximum likelihood estimation), hypothesis testing (e.g. Neyman-Pearson theorem, uniformly most powerful tests) and Bayesian inference (e.g. Bayesian priors, posteriors and estimators). Students are required to use the advanced statistical methodologies to perform data analysis and judge the appropriateness and effectiveness of tests.

Upon completion of this module, students should be able to:

1. understand the concepts of probability theories and distributions, advanced techniques of parameter estimation and the criteria for assessing an estimator;


2. perform tests of hypothesis and judge the appropriateness and goodness of tests;


3. understand the concepts and theories of Bayesian inference; and


4. perform data analysis with/without the use of computer software.