Markov chain Monte Carlo methods

One ongoing research project concerns a new Markov chain Monte Carlo (MCMC) method for drawing samples from a posterior distribution when the likelihood function or a part of the prior distribution is only specified up to a normalizing constant. Perfect simulation plays an important role in this connection. Another research project concerns ergodic averages for monotone functions using upper and lower dominating processes. In cooperation with Kerrie Mengersen, Newcastle University, Australia; Anthony N. Pettitt and Robert W. Reeves, Queensland University of Technology, Australia. Supported by MaPhySto and the Danish Natural Science Research Council