Bayesian statistics is widely used framework for handling complex data and phenomena with uncertainty. The solution, or inference, is based in mathematical terms on a multidimensional integral, which cannot be solved exactly in most cases. Monte Carlo methods, which are based on random numbers, usually produce an approximation which is accurate enough for practical purposes.
Many Monte Carlo methods, including the popular Markov chain Monte Carlo (MCMC), work well with small data sets, but are problematic when data size increases. The project develops new Monte Carlo inference methods, which are suitable with bigger data sets. The methods are designed to be used efficiently with parallel and distributed computing facilities. The developed methods will be applied in the project for instance with drug response prediction in childhood leukaemia, forest inventories and assessment of wildlife populations.