View Specification DocumentStatistical Inference (DD80) STATS3015
- Academic Session: 2026-27
- School: School of Mathematics and Statistics
- Credits: 20
- Level: Level 3 (SCQF level 9)
- Typically Offered: Runs Throughout Semesters 1 and 2
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
To present the fundamental principles of statistical inference, starting from the likelihood and the large sample results that are widely used in practice, as well as introducing methods of modern Bayesian inference, with an emphasis on practical issues and applications.
Timetable
Lectures: 30 hours (1-2 hours per week over two semesters)
Tutorials: 7 hours (over two semesters)
6 2-hour computer-based practical sessions over two semesters
Requirements of Entry
The normal requirement is that students should have been admitted to the third year of a Designated Degree programme in Statistics.
Excluded Courses
STATS5028 Statistical Inference (Level M)
STATS5014 Bayesian Statistics (Level M)
STATS4012 Statistical Inference
Co-requisites
The courses prescribed in the Designated Degree programme to which the student has been admitted.
Assessment
60 minute class test (20%)
120-minute, end-of-course examination (80%)
Main Assessment In: April/May
Course Aims
To establish an understanding of the fundamental principles of likelihood-based and Bayesian inference that are widely used in practice. To introduce students to the main ideas of modern likelihood-based and Bayesian statistics, and to illustrate formulation and analysis of statistical models in both frameworks, and implementation of these in statistical programming software.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ write down the likelihood and perform likelihood-based inference for a variety of statistical models
■ explain the choice of prior distribution and describe the rules for updating prior distributions in the presence of data, and for calculating posterior predictive distributions in a Bayesian framework
■ maximise likelihoods through numerical methods
■ summarise model results using point and interval estimates for both likelihood and Bayesian frameworks
■ test hypotheses about model parameters, and calculate power in a likelihood-based framework
■ discuss the principles behind the bootstrap and apply this technique to practical problems
■ understand the implementation of likelihood-based and Bayesian models using statistical programming languages