STAT40850 Bayesian Analysis (online)

Academic Year 2022/2023

This module will provide an introduction to Bayesian analysis with an emphasis on concepts in Bayesian theory and practice. A focus throughout will be on statistical programming via the Stan probabilistic programming language.

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Curricular information is subject to change

Learning Outcomes:

By the end of this module students should be able to understand and implement Bayesian statistical methods to a wide variety of data sets. They should be able to check the model and give a critique of the Bayesian process as opposed to its Frequentist counterpart.

Indicative Module Content:

Indicative content covered in this moduel will include:
+ A recap of the some basic concepts in probability theory.
+ Introduction to Bayesian statistics
+ Bayesian linear regression
+ Hierarchical models
+ Model comparison

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities

24

Autonomous Student Learning

72

Online Learning

24

Total

120

Approaches to Teaching and Learning:
Video lectures posted each week that walk through module content, blending theory with applications.
Each chapter in the module will be accompanied by Stan code which will allow students to implement the material developed in the video lectures. 
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
Incompatibles:
STAT40380 - Bayesian Analysis, STAT40390 - Bayesian Analysis


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Assignments will be a mix of theory and computer based problem sheets. Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

100


Carry forward of passed components
No
 
Resit In Terminal Exam
Summer 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.

Statistical Rethinking by McElreath
A first course in Bayesian Statistical methods by Hoff
Bayesian Data Analysis by Gelman, Carlin, Stern, and Rubin.
Name Role
Dr Riccardo Rastelli Lecturer / Co-Lecturer
Mr John O'Sullivan Tutor