Presented by:
Francois Bachoc Université de Toulouse
Date:
Friday 1st June 2018 - 11:00 to 13:00
Venue:
INI Seminar Room 2
Abstract:
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.
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