MCQMC Conference 2016
The 12th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing is a biennial meeting attracting 150 to 200 mathematicians, computer scientists, statisticians and researchers in related fields. This year's event is being organized by members of the Departments of Statistics and Management Science and Engineering and will be held at Stanford's Li Ka Shing Center. Plenary talks are expected from
- Christoph Aistleitner, Graz University of Technology, Austria
- Jose Blanchet, Columbia University
- Nicolas Chopin, Paris Graduate School of Economics, Statistics and Finance (ENSAE)
- Arnaud Doucet, Oxford University
- Peter Frazier, Uber, San Francisco
- Michael Jordan, UC Berkeley
- Frances Kuo, University of New South Wales, Australia
- Christiane Lemieux, University of Waterloo, Canada
- Dirk Nuyens, KU Leuven, Belgium
- Andrew Stuart, Warwick University
In a nutshell, MC methods study complex systems by simulations fed by computer-generated pseudorandom numbers. QMC methods replace these random numbers by more evenly distributed (carefully selected) numbers to improve their effectiveness. A large variety of special techniques are developed and used to make these methods more effective in terms of speed and accuracy. The conference focuses primarily on the mathematical study of these techniques, their implementation and adaptation for concrete applications, and their empirical assessment. The program will begin with tutorials in the afternoon of Sunday, August 14, with a banquet planned for the Wednesday evening, August 17.