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SUMMARY:Consistency of stepwise uncertainty reduction strategies for Gauss
 ian processes - Francois Bachoc (Université de Toulouse)
DTSTART:20180601T100000Z
DTEND:20180601T120000Z
UID:TALK107251@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:In the first part of the talk\, we will introduce spatial Gaus
 sian processes. Spatial Gaussian processes are widely studied from a stati
 stical point of view\, and have found applications in many fields\, includ
 ing geostatistics\, climate science and computer experiments. Exact infere
 nce can be conducted for Gaussian processes\, thanks to the Gaussian condi
 tioning theorem. Furthermore\, covariance parameters can be estimated\, fo
 r instance by Maximum Likelihood.  In the second part of the talk\, we wil
 l introduce a class of iterative sampling strategies for Gaussian processe
 s\, called &#39\;stepwise uncertainty reduction&#39\; (SUR). We will give 
 examples of SUR strategies which are widely applied to computer experiment
 s\, for instance for optimization or detection of failure domains. We will
  provide a general consistency result for SUR strategies\, together with a
 pplications to the most standard examples.  <br><br><br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
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