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SPDE scaling limits of an Markov chain Montecarlo algorithm

Presented by: 
J Mattingly [Duke]
Date: 
Monday 28th June 2010 - 14:10 to 15:00
Venue: 
INI Seminar Room 1
Abstract: 
I will discuss how a simple random walk metropolis algorithm converges to an SPDE as the dimension of the sample space goes to infinity. I will discuss how this the limiting SPDE gives insight into how one should tune the algorithm to obtain an asymptotically optimal mixing rate. This is joint work with Andrew Stuart and Natesh Pialli.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons