Course Details in 2024/25 Session

Module Title	LM Computational Statistics
School	Mathematics
Department	Mathematics
Module Code	06 40201
Module Lead	TBC
Level	Masters Level
Credits	10
Semester	Semester 1
Pre-requisites
Co-requisites
Restrictions	None
Contact Hours	Lecture-24 hours Guided independent study-76 hours Total: 100 hours
Exclusions
Description	This module is designed to equip students with the essential skills needed to tackle complex statistical problems through the application of numerical methods. By delving into computationally intensive statistical techniques, this module seeks to highlight the pivotal role that computation plays in the realm of discovery. The curriculum encompasses a wide array of critical subjects, including numerical optimization within statistical inference, featuring the expectation-maximization (EM) algorithm. It also covers topics on random number generation, Monte Carlo methods, Variation reduction techniques, and randomization techniques. Furthermore, students will gain proficiency in statistical programming. Flexibility is woven into the course structure, allowing for the exploration of additional pertinent topics. Practical computer assignments will be integrated, providing hands-on experience in applying these numerical methods to real-world statistical challenges.
Learning Outcomes	By the end of the module students should be able to: Understand the fundamental computational methods in Statistics and the related theories. Gain proficiency in programming language such as R, Matlab or Python for data processing and statistical analysis. Implement statistical algorithms like statistical sampling, maximum likelihood estimation, and so on. Apply the computational methods to a range of real-world problems.
Assessment
Assessment Methods & Exceptions	Assessment: 80% examination, 20% coursework. Reassessment: Resit exam
Other
Reading List