Statistical Inference (L.7) (867G1)

15 credits, Level 7 (Masters)

Spring teaching

This module will include:

  • Part 0: Revision of Probability a. Random Variables and probability distributions. b. Revision of some well known probability distributions c. Expectation and interpretation of moments. d. Conditional Probability and Bayes’ rule e. Conditional Expectation and properties.
  • Part 1: Frequentist Statistics a. Likelihood, Sufficiency and Ancillarity. b. Point estimators c. Hypothesis Testing d. Interval estimators (confidence intervals and their connection with hypothesis tests) e. Asymptotic Theory (consistency, asymptotic normality, chi square approximation).
  • Part 2: Bayesian Statistics a. The Bayesian Paradigm b. Bayesian Models c. Prior Distributions.
  • Part 3: Model Selection a. Frequentist Model Selection b. Bayesian Model selection and Bayes Factors.

Throughout this module, numerous practical real-world examples will be discussed during practical sessions and analysed using the R programming language.


85%: Lecture
15%: Practical


20%: Coursework (Portfolio, Problem set, Software exercise)
80%: Examination (Computer-based examination)

Contact hours and workload

This module is approximately 150 hours of work. This breaks down into about 33 hours of contact time and about 117 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.

This module is running in the academic year 2021/22. We also plan to offer it in future academic years. However, we are constantly looking to improve and enhance our courses. There may be changes to modules in response to student demand or feedback, changes to staff expertise or updates to our curriculum. We may also need to make changes in response to COVID-19. We’ll make sure to let our applicants know of material changes to modules at the earliest opportunity.


This module is offered on the following courses: