The best we can do with MCMC, and how to do better.

Monday, June 6, 2011 - 1:45pm - 2:45pm
Keller 3-180
Colin Fox (University of Otago)
Sample-based inference is a great way to summarize inverse and predictive distributions arising in large-scale applications. The best current technology for drawing samples are the MCMC algorithms, with the latest algorithms enabling comprehensive solution of substantial geophysical problems. However, for the largest-scale applications the geometric convergence of MCMC needs to be improved upon. A source of ideas are the algorithms from computational optimization. Developing the computational science of sampling algorithms is essential, for which a suite of test problems, using low-level mid-level and high-level representations, could be useful in focusing efforts in the community.
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