Multilevel Markov-Chain Monte Carlo Methods for Large Scale Problems
Monte Carlo methods play a central role in stochastic uncertainty quantification and data assimilation. In particular Markov chain Monte Carlo methods are of great interest also in the atmospheric sciences. However, they are notorious for their slow convergence and high computational cost. In this talk I will present revolutionary recent developments to mitigate and overcome this serious problem using a novel multilevel strategy and deterministic sampling rules. The talk will focus on methodology. The applications are so far mainly coming from other fields.