STAT40850

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:

Approaches to Teaching and Learning:

Requirements, Exclusions and Recommendations

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

Module Requisites and Incompatibles

Assessment Strategy

Feedback Strategy/Strategies

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

How will my Feedback be Delivered?

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