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.

Teaching

85%: Lecture
15%: Practical

Assessment

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.

We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2022/23. However, there may be changes to these modules in response to COVID-19, staff availability, student demand or updates to our curriculum. We’ll make sure to let our applicants know of material changes to modules at the earliest opportunity.