MTH4089 Computational statistical inference

When you finish this unit, you should be able to:

1. 1

   Apply sophisticated computational statistical inference in a wide range of application problems that require the integration of mathematical modelling with observed data to provide credible interpretation of the underlying system.

2. 2

   Explain the roles of likelihood models, missing data, and Bayesian inference and formalise parameter estimation problems in complex applications using these concepts.

3. 3

   Develop and apply advanced expectation-maximization methods to missing data problems.

4. 4

   Use the principle of Bayesian inference and apply expert computational methods to estimate parameters of statistical models and mathematical models.

5. 5

   Implement advanced computational methods used in statistical inference, including importance sampling, filtering, and Markov chain Monte Carlo, and understand the asymptotic behaviour of these methods.

6. 6

   Apply machine learning tools such as classification, Gaussian processes, and kernel methods to analyse and interpret complicate data sets and understand the computational aspects of these tools.