Campuses:

Gaussian Process

Tuesday, April 24, 2018 - 1:30pm - 2:00pm
Charlotte Haley (Argonne National Laboratory)
We propose a fully spatiotemporal approach for identifying spatially varying modes of oscillation
Wednesday, June 12, 2013 - 11:00am - 11:50am
Victor Zavala (Argonne National Laboratory)
We review applications and algorithmic challenges of Gaussian Process (GP) modeling. GP is a powerful and flexible uncertainty quantification and data analysis technique that enables the construction of complex models without the need to specify algebraic relationships between variables. This is done by working directly in the space of the kernel or covariance matrix. In addition, it derives from a Bayesian framework and, as such, it naturally provides predictive distributions.
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