This one-day workshop, associated with the 2010 Neural Information Processing Systems (NIPS) conference, took place on Friday December 10, 2010 in Whistler, British Columbia. The workshop focussed on the practical application of modern Monte Carlo techniques to problems of interest in machine learning and beyond. We had six invited talks, with speakers focusing on the real-world aspects of performing inference with Monte Carlo. We also had a wide range of contributed abstracts, which were presented in the poster session.
Monte Carlo methods have been the dominant form of approximate inference for Bayesian statistics over the last couple of decades. Monte Carlo methods are interesting as a technical topic of research in themselves, as well as enjoying widespread practical use. In a diverse number of application areas Monte Carlo methods have enabled Bayesian inference over classes of statistical models which previously would have been infeasible. Despite this broad and sustained attention, it is often still far from clear how best to set up a Monte Carlo method for a given problem, how to diagnose if it is working well, and how to improve under-performing methods. The impact of these issues is even more pronounced with new emerging applications. This workshop is aimed equally at practitioners and core Monte Carlo researchers. For practitioners we hope to identify what properties of applications are important for selecting, running and checking a Monte Carlo algorithm. Monte Carlo methods are applied to a broad variety of problems. The workshop aims to identify and explore what properties of these disparate areas are important to think about when applying Monte Carlo methods.
- Ryan Prescott Adams, University of Toronto
- Mark Girolami, University College London
- Iain Murray, University of Edinburgh
- Arnaud Doucet, University of British Columbia
- Chris Holmes, University of Oxford
- Radford M. Neal, University of Toronto
- Carl Edward Rasmussen, University of Cambridge
- Gareth O. Roberts, University of Warwick