Course Details in 2025/26 Session

Module Title	LH Bayesian Statistics
School	Mathematics
Department	Mathematics
Module Code	06 31302
Module Lead	Yin Jing
Level	Honours Level
Credits	10
Semester	Semester 2
Pre-requisites
Co-requisites
Restrictions	Available only to students on the JI dual degree pro-grammes
Exclusions
Description	Bayesian Statistics is a distinctive branch of mathematical statistics. This module focuses on Bayesian statistical in-ference theory, its methods and concepts. The differences between Bayesian methods and classical methods are presented. Students will systematically master the underlying theory, methods, and applications of Bayesian statistics. The main contents are: the concept of priori and posterior distributions, methods of calculation for the posterior distri-bution, Bayesian estimation and hypothesis testing, and Bayesian statistical decision-making methods.
Learning Outcomes	By the end of the module students should be able to: Understand the theory and principles of Bayesian statistical inference. Understand the difference between Bayesian and classical statistical methods Apply Bayesian statistical techniques to solve practical problems. Demonstrate competence in the methods of Bayesian statistical inference.
Assessment
Assessment Methods & Exceptions	Assesment: Assignments (30%) Final Exam (70%): a 2 hour examination Re-assessment (where allowed): a 2-hour resit examination (100%)
Other
Reading List