Statistical Inference (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 (Problem Set)
80%: Examination (Unseen examination)

Contact hours and workload

This module is 150 hours of work. This breaks down into 33 hours of contact time and 117 hours of independent study.

This module is running in the academic year 2019/20. We also plan to offer it in future academic years. It may become unavailable due to staff availability, student demand or updates to our curriculum. We’ll make sure to let our applicants know of such changes to modules at the earliest opportunity.