SDSC3005 - Computational Statistics | ||||||||||
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| * The offering term is subject to change without prior notice | ||||||||||
Course Aims | ||||||||||
This course introduces students to algorithms and techniques for statistical computing and their implementations through R software. Students will learn important computational statistics methods such as the EM algorithm, Fishera??s scoring, Monte Carlo simulation, Markov chain Monte Carlo, and bootstrap. Additionally, students will learn statistical applications of these methods, the key advantages of using each method, and how they can be coded in R. Efficient programming methods for R will be taught. Therefore, students gain knowledge of many different tools that can be combined to solve statistical computing problems. Assignments will involve the use R. | ||||||||||
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 | ||||||||||
| SDSC3005.pdf | ||||||||||
