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SDSC3105 - Bayesian Analysis

Offering Academic Unit
School of Data Science
Credit Units
3
Course Duration
One Semester
Pre-requisite(s)
Course Offering Term*:
Semester A 2023/24

* The offering term is subject to change without prior notice
 
Course Aims

This course aims at offering students rigorous knowledge of Bayesian statistical theory and methods, developing students' abilities of interpreting and communicating results, as well as training students to apply software packages such as R or Matlab to fit Bayesian models and conduct Bayesian analyses.


Assessment (Indicative only, please check the detailed course information)

Continuous Assessment: 60%
Examination: 40%
Examination Duration: 2 hours
Note: To pass the course, apart from obtaining a minimum of 40% in the overall mark, a student must also obtain a minimum mark of 30% in both continuous assessment and examination components.
 
Detailed Course Information

SDSC3105.pdf