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STAT40850

Academic Year 2024/2025

Bayesian Analysis (online) (STAT40850)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Alberto Caimo
Trimester:
Spring
Mode of Delivery:
Online
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 with an emphasis on concepts in Bayesian theory and practice. A focus throughout will be on statistical programming via the Stan probabilistic programming language.

About this Module

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 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
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.
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