STAT40950 Adv Bayesian Analysis (online)

Academic Year 2021/2022

Bayesian analysis allows us to incorporate uncertainty from multiple sources in a coherent way. We can therefore construct complex models for high dimensional data, however inference for such models may be cumbersome and require specialised approaches. This module will focus on how to build such complex models based on prior beliefs and how to use cutting edge
computational methods to perform fast and accurate inference. How to achieve automatic tuning of algorithms will also be explored.

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Curricular information is subject to change

Learning Outcomes:

By the end of this module you will be familiar with the theory and implementation of advanced Bayesian inferential methods that are optimised to explore posterior distributions effectively, including when the likelihood is intractable. You will know which approaches to use and why and will have experience in fitting complex models to highly heterogeneous / structured data.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Total

24
Approaches to Teaching and Learning:
Lectures, tutorials, enquiry and problem-based learning. 
Requirements, Exclusions and Recommendations
Learning Requirements:

Students will require prior knowledge of Bayesian Statistics and also an advanced understanding of probability and statistics.


Module Requisites and Incompatibles
Required:
STAT40850 - Bayesian Analysis (online)


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Mini Projects Varies over the Trimester n/a Standard conversion grade scale 40% No

100

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

• Group/class feedback, post-assessment

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