Bayesian nonparametric methods with Dirichlet process and Gaussian priors, among others, are widely used in practice and are part of some machine learning methods. However, it is known that they can lead to inconsistent inference. Hence, it is important to study large sample properties of inference based on these models, such the rate of contraction of the posterior distribution and local concentration of the posterior around the true parameter (known as the Bernstein-von Mises theorem). The project will be supervised by Dr Natalia Bochkina.
• A Bachelor’s degree in Statistics, Mathematics, Physics, Computer Science or similar (a First Class or good Upper Second Class Honours degree, or the equivalent from an overseas university);
• Strong mathematical background, particularly in functional analysis and probability theory
• Strong verbal and written communication skills in English.
• Knowledge of Bayesian methods
This project is funded by a University of Edinburgh scholarship which fully covers the cost of tuition fees and provides an annual stipend. This scholarship is open to home, EU, and overseas students.