Course co-ordinator(s): Dr Marcelo Pereyra (Edinburgh), Dr Mahendran Shitan (Malaysia), Dr Soo Huei Ching (Malaysia).
Aims:
In this module students will
• develop an understanding of the different methodologies of statistical inference
• develop skills in practical, computer-based estimation and inference
• develop report writing and presentation skill
• develop independent research skills
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Location: Edinburgh, Malaysia.
Semester: 1.
Syllabus:
• Inference and decision making
• Parameter estimation
• Likelihood
• Bayesian estimation and credibility theory
• Hypothesis testing
• Project preparation
• Applied statistical project
Learning Outcomes:
On completion of this course, students will be able to demonstrate:
• understanding of the theoretical bases of three main approaches to statistical inference and the relations
between them
• ability to apply all three main approaches to inference in a number of examples
• ability to understand and assess applicability and limitations of these approaches working with data sets in a
practical setting
• critical analysis of quantities of interest and conclusions made using statistical inference
On completion of this course, students will:
• develop their problem-solving skills
• gain ability to critically understand and apply relevant approaches to statistical inference in a practical setting
Assessment Methods: Due to covid, assessment methods for Academic Year 2021-22 may vary from those noted on the official course descriptor. Please see the Computer Science Course Weightings and the Maths Course Weightings for 2020-21 Semester 1 assessment methods.
SCQF Level: 11.
Credits: 15.
Other Information
Help: If you have any problems or questions regarding the course, you are encouraged to contact the lecturer
Canvas: further information and course materials are available on Canvas