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Non-Reversible Markov Chains for Monte Carlo Sampling, 21 - 23 September 2015



Markov Chain Monte Carlo (MCMC) methods are of fundamental importance to various scientific fields, such as statistics, statistical physics, molecular dynamics and machine learning. The usefulness of these MCMC methods depends for a large part on their computational efficiency. Traditionally MCMC algorithms are in the particular class of reversible Markov chains. Intuitively this means that by watching a video of the algorithm as it progresses, it is impossible to tell whether the video is playing forward or backward in time.

In contrast, Non-reversible Markov chains can be thought of as having a certain sense of direction or vorticity. It turns out that these may have significant benefits in terms of computational efficiency over their reversible counterparts. It will be the aim of this workshop to explore:

  • theoretical properties of non-reversible Markov chains that are important in determining their usefulness for MCMC;
  • aspects of augmented state space algorithms such as Hamiltonian Monte Carlo and their relations to non-reversible Markov processes;
  • theory and practice of design and implementation of efficient non-reversible Markov chains.

Keynote Speakers 

Alexandros Beskos, Department of Statistical Science, University College London, UK

Michael Betancourt, University of Warwick

Chii-Ruey Hwang, Institute of Mathematics, Academia Sinica, Taiwan

Tony Lelièvre, Ecole des Ponts ParisTech

Ravi Montenegro, Mathematical Sciences, UML, USA

Michela Ottobre, Heriot Watt University, Edinburgh

Grigoris Pavliotis, Applied Mathematics and Mathematical Physics Section­­­­, Imperial College London, UK

Luc Rey-Bellet, Department of Mathematics and Statistics, University of Massachusetts, USA

Gareth Roberts, Department of Statistics, University of Warwick

Marija Vucelja, Department of Physics, University of Virginia


Correspondance should be sent to j.bierkens (at)




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