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Curricular information is subject to change
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 Type||Hours|
|Specified Learning Activities||
|Autonomous Student Learning||
You should have completed a basic course in statistics including probability, inference, hypothesis testing, estimation and regression.
|Resit In||Terminal Exam|
|Summer||Yes - 2 Hour|
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
|Dr Riccardo Rastelli||Lecturer / Co-Lecturer|
|Dr John O'Sullivan||Tutor|