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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20191119T095731Z
DTSTART:20191203T120000Z
DTEND:20191203T130000Z
SUMMARY:**CANCELLED** Richard Nickl - Statistical guarantees for the Baye
 sian approach to inverse problems
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}ujs-k0mgnbm
 k-dphu3h
DESCRIPTION:**This seminar has been cancelled due to the industrial actio
 n** We are planning to reschedule for the spring semester.\n\nAbstract: 
 Bayes methods for inverse problems have become very popular in applied m
 athematics in the last decade after seminal work by Persi Diaconis and A
 ndrew Stuart. They provide reconstruction algorithms as well as in-built
  “uncertainty quantification” via Bayesian credible sets\, and particula
 rly for Gaussian process priors can be efficiently implemented by MCMC m
 ethodology. For linear inverse problems\, they are closely related to cl
 assical penalised least squares methods and thus not fundamentally new\,
  but for non-linear and non-convex problems\, they give genuinely distin
 ct and computable algorithmic alternatives that cannot be studied by var
 iational analysis or convex optimisation techniques. In this talk we wil
 l discuss recent progress in Bayesian non-parametric statistics that all
 ows to give rigorous statistical guarantees for posterior consistency in
  such models\, and illustrate the theory in a variety of concrete non-li
 near inverse problems arising with partial differential equations and X-
 ray transforms.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Frank Adams 1\, Alan Turing Building\, Manchester
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