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BEGIN:VEVENT
DTSTAMP:20260624T175854Z
DTSTART;VALUE=DATE-TIME:20110614T120000
DTEND;VALUE=DATE-TIME:20110614T130000
SUMMARY:MCMC using lessons from CSE
TZID:Europe/London
UID:20110614-094d43d5385c299a01386c3bd8c77277@warwick.ac.uk
CREATED:20110404T114347Z
DESCRIPTION:(Or: sample-based inference in large-scale inverse problems u
 sing algorithms developed for computational optimization) By Colin Fox A
 bstract: The Metropolis (Hastings-Green) algorithm for MCMC was invented
  in the 1950’s and has changed little since. In contrast\, algorithms 
 for optimization that were also invented in the 1950’s have seen drama
 tic improvement. We look to steal the developments in computational opti
 mization and apply them to sampling of probability distributions. The co
 nnection between sampling and optimization is already close. For example
 \, Gibbs sampling of Gaussian distributions is exactly Gauss-Seidel solu
 tion of linear equations – the iteration operators are identical and c
 onvergence factors are identical. Polynomial acceleration developed for 
 iterative solvers can also accelerate Gibbs sampling giving optimal conv
 ergence. I will show some applications in large-scale inverse problems t
 hat motivated this (ongoing) work\, including sample-based inference in 
 image deblurring with non-negativity constraints computed using a sampli
 ng version of the gradient-projected conjugate gradient algorithm. organ
 ised by Andrew Stuart LUNCH TO BE SERVED IN COMPLEXITY's COMMON ROOM
LOCATION:D1.07
URL:/fac/cross_fac/complexity/newsandevents/calendar
 /tbc
ATTACH:/fac/cross_fac/complexity/newsandevents/calen
 dar/tbc
CATEGORIES:Seminar Room D1.07
LAST-MODIFIED:20120709T145409Z
ORGANIZER;CN="":
END:VEVENT
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