By the end of the course, students should be able to:
- identify which distribution is appropriate for certain types of data
- estimate parameters and their associated uncertainty via likelihood methods, and interpret these values in the context of real-world problems.
- incorporate prior information into common statistical problems and obtain posterior probability distributions of parameters of interest.

This module contains three parts: probability distributions, likelihood, and Bayesian inference. In the first part, students are introduced to the main probability distributions, including the Gaussian, Binomial, Poisson and Gamma distributions and shown their practical use and theoretical implications. In part two, students are introduced to the concept of likelihood and its use in estimating parameters of distributions and their associated uncertainty. In part 3, students are shown how to incorporate prior information and structure via Bayesian methods.

Calculus: familiarity with differentiation and integration. Students are required to have completed an introductory statistics module such as STAT10060 or STAT40720.

• Group/class feedback, post-assessment

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