Statistical Inference (L.7) (867G1)
15 credits, Level 7 (Masters)
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 and assessment
We’re currently reviewing teaching and assessment of our modules in light of the COVID-19 situation. We’ll publish the latest information as soon as possible.
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 2020/21. We also plan to offer it in future academic years. However, there may be changes to this module in response to COVID-19, or due to 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.
It may not be possible to take some module combinations due to timetabling constraints. The structure of some courses means that the modules you choose first may determine whether later modules are core or optional.
This module is offered on the following courses: