Francois Bachoc
Consistency of stepwise uncertainty reduction strategies for Gaussian processes
Was |
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Wann |
27.04.2018 von 12:00 bis 13:00 |
Wo | Eckerstraße 1, Raum 404, 4. OG |
Termin übernehmen |
vCal iCal |
In the first part of the talk, we will introduce spatial Gaussian processes.
Spatial Gaussian processes are widely studied from a statistical point
of view, and have found applications in many fields, including
geostatistics, climate science and computer experiments. Exact inference
can be conducted for Gaussian processes, thanks to the Gaussian
conditioning theorem. Furthermore, covariance parameters can be
estimated, for instance by Maximum Likelihood.
In the second part of the talk, we will introduce a class of iterative
sampling strategies for Gaussian processes, called 'stepwise uncertainty
reduction' (SUR). We will give examples of SUR strategies which are
widely applied to computer experiments, for instance for optimization or
detection of failure domains. We will provide a general consistency
result for SUR strategies, together with applications to the most
standard examples.