Efficient Sampling in Bayesian Models

Project level: PhD, Masters, Honours

Many real-world statistical problems are nowadays solved via Bayesian models, where the aim is to sample from some posterior distribution. For high-dimensional problems this can be a very difficult or time-consuming task. The purpose of this project is to investigate and develop efficient Monte Carlo sampling procedures, such as the recently discovered method in [Botev, Z.I., Kroese, D.P. (2008). An Efficient Algorithm for Rare-event Probability Estimation, Combinatorial Optimization, and Counting. Methodology and Computing in Applied Probability 10 (4)]. Applications range from computational biology and medicine to financial engineering and risk analysis; a spin-off will be the development of software in these areas.