Handbook

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.

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

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

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

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.

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.