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STAT20180

Academic Year 2024/2025

Introduction to Bayesian Analysis (STAT20180)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
2 (Intermediate)
Credits:
5
Module Coordinator:
Professor Claire Gormley
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module will provide an introduction to Bayesian analysis. After an overview of foundational concepts in probability theory, you will be introduced to concepted in Bayesian statistics including, prior and posterior distribution as well as means by which you can summarise the posterior distribution. We will also explain how to derive posterior predictive distributions. Throughout we will illustrate how Monte Carlo methods can be used to approximate several key quantities of interest. Throughout all important concepts will be explained via examples.

About this Module

Learning Outcomes:

By the end of the course students should have a good understanding of the key concepts and ideas in Bayesian statistical modelling including, credible intervals; posterior predictive distributions; posterior model checks. Students should be familar also with the idea of Monte Carlo sampling as a means for approximate inference.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Tutorial

5
Computer Aided Lab

5
Specified Learning Activities

10
Autonomous Student Learning

75
Total

119

Approaches to Teaching and Learning:
Lectures will involves a mix of concepts and theory, blended throughout with example exercises.

Lecture material will be reinforced through computer lab classes where students will be stepped through the examples developed in class. Each lab class will be completed with an assignment.

The material developed in the lecture classes will be further enhanced through fortnightly tutorials. These classes will summarise the main content in the lectures and provide solutions to some selected exercises set in the lecture classes.

Requirements, Exclusions and Recommendations
Learning Recommendations:

You should have completed a basic course in statistics including probability, inference, hypothesis testing, estimation and regression.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered

Not yet recorded.

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

A First Course in Bayesian Statistical Methods by Peter D. Hoff.
Bayesian Statistics: An Introduction by Peter M. Lee.
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