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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 concepts in Bayesian statistics including prior and posterior distributions 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 key distributional quantities of interest. Throughout all important concepts will be explained via real, data-based examples and through the R statistical computing software.

About this Module

Learning Outcomes:

By the end of the module 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 also be familiar with the idea of Monte Carlo sampling as a means for approximate inference. Students will know how to use Bayesian approaches to analyse data and how to implement such analyses in statistical computing software.

Indicative Module Content:

1. Recap of Probability theory.
2. Bayesian inference for a proportion.
3. Introduction to Monte Carlo inference.
4. Bayesian inference for count data.
5. Bayesian inference for the normal distribution.
6. Towards Bayesian data analysis.

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 introduce concepts and theory, which will be illustrated throughout by the analysis of example, real-life data.

Lecture material will be reinforced through computer lab classes where students will step through the examples covered in class. Each computer lab class will be have an associated, credit-bearing assignment.

The material developed in the lectures will be further enhanced through fortnightly tutorials. These tutorials will summarise the main content in the lectures and assist students in working through solutions to some selected exercises.

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
Assignment(Including Essay): Each of 5 computer lab classes (in weeks 3, 5, 7, 9, 11) has a take-home assignment, due the following week & worth 8% ,which requires written, numeric and code-based answers to a series of questions. Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Standard conversion grade scale 40% No
40
 No
Exam (In-person): Exam (In-person): Two hour, closed-book, written examination that will take place in an exam centre during the end of trimester exam period. Solutions will require written and numerate responses. End of trimester
Duration:
2 hr(s)
 Standard conversion grade scale 40% No
60
 No

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?

Written feedback through a VLE as well as general oral feedback in class.

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

Name Role
Blerta Begu Tutor
Ms Courtney Clarke Tutor
Brian O'Sullivan Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 10:00 - 10:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 10:00 - 10:50
Spring Tutorial Offering 1 Week(s) - 23, 25, 29, 31, 33 Wed 14:00 - 14:50
Spring Tutorial Offering 2 Week(s) - 23, 25, 29, 31, 33 Fri 13:00 - 13:50
Spring Tutorial Offering 3 Week(s) - 23, 25, 29, 31, 33 Wed 12:00 - 12:50
Spring Computer Aided Lab Offering 1 Week(s) - 22, 24, 26, 30, 32 Tues 11:00 - 11:50
Spring Computer Aided Lab Offering 2 Week(s) - 22, 24, 26, 30, 32 Thurs 13:00 - 13:50
Spring Computer Aided Lab Offering 3 Week(s) - 22, 24, 26, 30, 32 Wed 12:00 - 12:50
Spring Computer Aided Lab Offering 4 Week(s) - 22, 24, 26, 30, 32 Wed 14:00 - 14:50
Spring Computer Aided Lab Offering 5 Week(s) - 22, 24, 26, 30, 32 Fri 13:00 - 13:50
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